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Monitore 25 Zoll und mehr
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Fernseher 102 bis 109 cm Bilddiagonale
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Samsung hat die Nase vorn. Die Stiftung Warentest hat acht Fernseher mit einer Bilddiagonale zwischen 40 un...
LED-Lampen Kerzenform, E14, 300 bis 470 Lumen
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Staubsauger ohne Beutel (Zyklon)
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Gütesiegel für Lebensmittel
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Zeige 7 von 7 Produkte
{"price-changing":0,"image":"https:\/\/image.vergleiche.ch\/small\/aHR0cHM6Ly93d3cuZ29vZGZvcm0uY2gvbWVkaWEvY2F0YWxvZy9wcm9kdWN0Ly9lL2kvZWlsZWVuLWNpcmN1bGFyLTIucG5n!aHR0cHM6Ly93d3cuZ29vZGZvcm0uY2gvbWVkaWEvY2F0YWxvZy9wcm9kdWN0Ly9lL2kvZWlsZWVuLWNpcmN1bGFyLTIucG5n","post_title":"SP01 EILEEN Runder Gartentisch aus HPL","deeplink":"https:\/\/www.awin1.com\/pclick.php?p=26410422955&a=401125&m=15031&pref1=sp01-eileen-runder-gartentisch-aus-hpl","labels":[],"brand_id":413108,"post_content":"Eileen is an elegant table designed for outdoors, with an aesthetic that will work well in both residential and commercial settings.The design in strongly defined by its frame - a central stretcher rail made from two folded stainless steel sections which connect to tapered blade legs with a disc shaped foot.PRODUCT CONSTRUCTION. Base made from stainless steel powder coated in Outdoor Black RAL9017 or Outdoor White RAL9002.Table top available in marble in Bianco Carrara, Nero Marquinia, Verde Guatemala or Rosso Levanto, in polished or honed finish, always sealed for outdoor use.Table top also available in High Pressure Laminate (HPL) in white or black, always with black edge.The table is available in three different sizes: 75 dining height, 75 bar height, and 90 dining height.The tops are available in two different shapes: round or square with soft rounded corners.","merchants_number":1,"ean":null,"category_id":1093,"size":null,"min_price":1681,"low_price_merchant_id":1087669,"ID":13620581,"merchants":["goodform"],"brand":"SP01","slug":"sp01-eileen-runder-gartentisch-aus-hpl","url":"\/haushalt\/produkt\/sp01-eileen-runder-gartentisch-aus-hpl\/","low_price_merchant_name":"Goodform"}
SP01
SP01 EILEEN Runder Gartentisch aus HPL
CHF 1,681.00
{"price-changing":0,"image":"https:\/\/image.vergleiche.ch\/small\/aHR0cHM6Ly93d3cuZ29vZGZvcm0uY2gvbWVkaWEvY2F0YWxvZy9wcm9kdWN0Ly9lL2kvZWlsZWVuLWNpcmN1bGFyLTIucG5n!aHR0cHM6Ly93d3cuZ29vZGZvcm0uY2gvbWVkaWEvY2F0YWxvZy9wcm9kdWN0Ly9lL2kvZWlsZWVuLWNpcmN1bGFyLTIucG5n","post_title":"SP01 EILEEN Runder Gartentisch aus HPL","deeplink":"https:\/\/www.awin1.com\/pclick.php?p=26410422955&a=401125&m=15031&pref1=sp01-eileen-runder-gartentisch-aus-hpl","labels":[],"brand_id":413108,"post_content":"Eileen is an elegant table designed for outdoors, with an aesthetic that will work well in both residential and commercial settings.The design in strongly defined by its frame - a central stretcher rail made from two folded stainless steel sections which connect to tapered blade legs with a disc shaped foot.PRODUCT CONSTRUCTION. Base made from stainless steel powder coated in Outdoor Black RAL9017 or Outdoor White RAL9002.Table top available in marble in Bianco Carrara, Nero Marquinia, Verde Guatemala or Rosso Levanto, in polished or honed finish, always sealed for outdoor use.Table top also available in High Pressure Laminate (HPL) in white or black, always with black edge.The table is available in three different sizes: 75 dining height, 75 bar height, and 90 dining height.The tops are available in two different shapes: round or square with soft rounded corners.","merchants_number":1,"ean":null,"category_id":1093,"size":null,"min_price":1681,"low_price_merchant_id":1087669,"ID":13620581,"merchants":["goodform"],"brand":"SP01","slug":"sp01-eileen-runder-gartentisch-aus-hpl","url":"\/haushalt\/produkt\/sp01-eileen-runder-gartentisch-aus-hpl\/","low_price_merchant_name":"Goodform"}
{"price-changing":0,"image":"https:\/\/image.vergleiche.ch\/small\/aHR0cHM6Ly9vczEubWVpbmVjbG91ZC5pby9iMTAxNTgvbWVkaWEvaW1hZ2UvOTkvNTgvNjgvNjA1Njk5MjMwMDAwMUFfNjAweDYwMC5qcGc=!aHR0cHM6Ly9vczEubWVpbmVjbG91ZC5pby9iMTAxNTgvbWVkaWEvaW1hZ2UvOTkvNTgvNjgvNjA1Njk5MjMwMDAwMUFfNjAweDYwMC5qcGc=","post_title":"Biomedical Nanotechnology","deeplink":"https:\/\/cct.connects.ch\/tc.php?t=116298C1969900829T&subid=9781493968381&deepurl=https%3A%2F%2Feuniverse.ch%2Fbuecher%2Fmathematik-naturwissenschaft-technik%2Fbiologie%2F377897%2Fbiomedical-nanotechnology-methods-and-protocols%3FsPartner%3Dtoppreise","labels":[],"brand_id":1,"post_content":"Preface...Table of Contents...Contributing Authors...1.\u00a0Quantification of siRNA Duplexes Bound to Gold Nanoparticle SurfacesJilian R. Melamed, Rachel S. Riley, Danielle M. Valcourt, Margaret M. Billingsley, Nicole L. Kreuzberger, and Emily S. Day2.\u00a0Ligand Exchange and 1H NMR Quantification of Single- and Mixed-Moiety Thiolated Ligand Shells on Gold NanoparticlesAshley M. Smith and Jill E. Millstone3.\u00a0Nanoparticle Tracking Analysis for Determination of Hydrodynamic Diameter, Concentration, and Zeta Potential of Polyplex NanoparticlesDavid R. Wilson and Jordan J. Green4.\u00a0Magnetic Characterization of Iron Oxide Nanoparticles for Biomedical ApplicationsLorena Maldonado-Camargo, Mythreyi Unni, and Carlos Rinaldi5.\u00a0Preparation of Magnetic Nanoparticles for Biomedical ApplicationsXiaolian Sun and Shouheng Sun6.\u00a0Brain-Penetrating Nanoparticles for Analysis of the Brain MicroenvironmentElizabeth Nance\u00a07.\u00a0Volumetric Bar-Chart Chips for BiosensingYujun Song, Ying Li, and Lidong Qin8.\u00a0qFlow Cytometry-Based Receptoromic Screening: A High-Throughput Quantification Approach Informing Biomarker Selection and Nanosensor DevelopmentSi Chen, Jared Weddell, Pavan Gupta, Grace Conard, James Parkin, and Princess I. Imoukhuede9.\u00a0Evaluating Nanoparticle Binding to Blood Compartment Immune Cells in High-Throughput with Flow CytometryShann S. Yu10.\u00a0A Gold@Polydopamine Core-Shell Nanoprobe for Long-Term Intracellular Detection of MicroRNAs in Differentiating Stem CellsChun Kit K. Choi, Chung Hang J. Choi, and Liming Bian11.\u00a0Antibody-Conjugated Single Quantum Dot Tracking of Membrane Neurotransmitter Transporters in Primary Neuronal CulturesDanielle M. Bailey, Oleg Kovtuna, and Sandra J. Rosenthal12.\u00a0Spectroscopic Photoacoustic Imaging of Gold NanorodsAustin Van Namen and Geoffrey P. Luke13.\u00a0Dual Wavelength-Triggered Gold Nanorods for Anti-Cancer TreatmentDennis B. Pacardo, Frances S. Ligler, and Zhen Gu14.\u00a0Photolabile Self-Immolative DNA-Drug NanostructuresXuyu Tan and Ke Zhang15.\u00a0Enzyme-Responsive Nanoparticles for the Treatment of DiseaseCassandra E. Callmann and Nathan C. Gianneschi16.\u00a0NanoScript: A Versatile Nanoparticle-Based Synthetic Transcription Factor for Innovative Gene ManipulationKholud Dardir, Christopher Rathnam, and KiBum Lee17.\u00a0Glucose-Responsive Insulin Delivery by Microneedle-Array Patches Loaded with Hypoxia-Sensitive VesiclesJicheng Yu, Yuqi Zhang, and Zhen Gu18.\u00a0Electrospun Nanofiber Scaffolds and their Hydrogel Composites for the Engineering and Regeneration of Soft TissuesOhan S. Manoukian, Rita Matta, Justin Letendre, Paige Collins, Augustus D. Mazzocca, and Sangamesh G. Kumbar19.\u00a0Application of Hydrogel Template Strategy in Ocular Drug DeliveryCrystal S. Shin, Daniela C. Marcano, Kinam Park, and Ghanashyam Acharya20.\u00a0High-Accuracy Determination of Cytotoxic Responses from Graphene Oxide Exposure using Imaging Flow CytometrySandra Vranic and Kostas Kostarelos21.\u00a0Air-Liquid Interface Cell Exposures to Nanoparticle AerosolsNastassja A. Lewinski, Nathan J. Liu, Akrivi Asimakopoulou, Eleni Papaioannou, Athanasios Konstandopoulos, and Michael Riediker22.\u00a0Returning to the Patent Landscapes for Nanotechnology: Assessing the Garden That It Has Grown IntoDiana M. Bowman, Douglas J. Sylvester, and Anthony D. Marino\u00a0","merchants_number":1,"ean":9781493968381,"category_id":103,"size":null,"min_price":132.5,"low_price_merchant_id":70255345,"ID":4909088,"merchants":["euniverse"],"brand":"undefined","slug":"biomedical-nanotechnology-1","url":"\/unterhaltung\/produkt\/biomedical-nanotechnology-1\/","low_price_merchant_name":"eUniverse"}
undefined
Biomedical Nanotechnology
CHF 132.50
{"price-changing":0,"image":"https:\/\/image.vergleiche.ch\/small\/aHR0cHM6Ly9vczEubWVpbmVjbG91ZC5pby9iMTAxNTgvbWVkaWEvaW1hZ2UvOTkvNTgvNjgvNjA1Njk5MjMwMDAwMUFfNjAweDYwMC5qcGc=!aHR0cHM6Ly9vczEubWVpbmVjbG91ZC5pby9iMTAxNTgvbWVkaWEvaW1hZ2UvOTkvNTgvNjgvNjA1Njk5MjMwMDAwMUFfNjAweDYwMC5qcGc=","post_title":"Biomedical Nanotechnology","deeplink":"https:\/\/cct.connects.ch\/tc.php?t=116298C1969900829T&subid=9781493968381&deepurl=https%3A%2F%2Feuniverse.ch%2Fbuecher%2Fmathematik-naturwissenschaft-technik%2Fbiologie%2F377897%2Fbiomedical-nanotechnology-methods-and-protocols%3FsPartner%3Dtoppreise","labels":[],"brand_id":1,"post_content":"Preface...Table of Contents...Contributing Authors...1.\u00a0Quantification of siRNA Duplexes Bound to Gold Nanoparticle SurfacesJilian R. Melamed, Rachel S. Riley, Danielle M. Valcourt, Margaret M. Billingsley, Nicole L. Kreuzberger, and Emily S. Day2.\u00a0Ligand Exchange and 1H NMR Quantification of Single- and Mixed-Moiety Thiolated Ligand Shells on Gold NanoparticlesAshley M. Smith and Jill E. Millstone3.\u00a0Nanoparticle Tracking Analysis for Determination of Hydrodynamic Diameter, Concentration, and Zeta Potential of Polyplex NanoparticlesDavid R. Wilson and Jordan J. Green4.\u00a0Magnetic Characterization of Iron Oxide Nanoparticles for Biomedical ApplicationsLorena Maldonado-Camargo, Mythreyi Unni, and Carlos Rinaldi5.\u00a0Preparation of Magnetic Nanoparticles for Biomedical ApplicationsXiaolian Sun and Shouheng Sun6.\u00a0Brain-Penetrating Nanoparticles for Analysis of the Brain MicroenvironmentElizabeth Nance\u00a07.\u00a0Volumetric Bar-Chart Chips for BiosensingYujun Song, Ying Li, and Lidong Qin8.\u00a0qFlow Cytometry-Based Receptoromic Screening: A High-Throughput Quantification Approach Informing Biomarker Selection and Nanosensor DevelopmentSi Chen, Jared Weddell, Pavan Gupta, Grace Conard, James Parkin, and Princess I. Imoukhuede9.\u00a0Evaluating Nanoparticle Binding to Blood Compartment Immune Cells in High-Throughput with Flow CytometryShann S. Yu10.\u00a0A Gold@Polydopamine Core-Shell Nanoprobe for Long-Term Intracellular Detection of MicroRNAs in Differentiating Stem CellsChun Kit K. Choi, Chung Hang J. Choi, and Liming Bian11.\u00a0Antibody-Conjugated Single Quantum Dot Tracking of Membrane Neurotransmitter Transporters in Primary Neuronal CulturesDanielle M. Bailey, Oleg Kovtuna, and Sandra J. Rosenthal12.\u00a0Spectroscopic Photoacoustic Imaging of Gold NanorodsAustin Van Namen and Geoffrey P. Luke13.\u00a0Dual Wavelength-Triggered Gold Nanorods for Anti-Cancer TreatmentDennis B. Pacardo, Frances S. Ligler, and Zhen Gu14.\u00a0Photolabile Self-Immolative DNA-Drug NanostructuresXuyu Tan and Ke Zhang15.\u00a0Enzyme-Responsive Nanoparticles for the Treatment of DiseaseCassandra E. Callmann and Nathan C. Gianneschi16.\u00a0NanoScript: A Versatile Nanoparticle-Based Synthetic Transcription Factor for Innovative Gene ManipulationKholud Dardir, Christopher Rathnam, and KiBum Lee17.\u00a0Glucose-Responsive Insulin Delivery by Microneedle-Array Patches Loaded with Hypoxia-Sensitive VesiclesJicheng Yu, Yuqi Zhang, and Zhen Gu18.\u00a0Electrospun Nanofiber Scaffolds and their Hydrogel Composites for the Engineering and Regeneration of Soft TissuesOhan S. Manoukian, Rita Matta, Justin Letendre, Paige Collins, Augustus D. Mazzocca, and Sangamesh G. Kumbar19.\u00a0Application of Hydrogel Template Strategy in Ocular Drug DeliveryCrystal S. Shin, Daniela C. Marcano, Kinam Park, and Ghanashyam Acharya20.\u00a0High-Accuracy Determination of Cytotoxic Responses from Graphene Oxide Exposure using Imaging Flow CytometrySandra Vranic and Kostas Kostarelos21.\u00a0Air-Liquid Interface Cell Exposures to Nanoparticle AerosolsNastassja A. Lewinski, Nathan J. Liu, Akrivi Asimakopoulou, Eleni Papaioannou, Athanasios Konstandopoulos, and Michael Riediker22.\u00a0Returning to the Patent Landscapes for Nanotechnology: Assessing the Garden That It Has Grown IntoDiana M. Bowman, Douglas J. Sylvester, and Anthony D. Marino\u00a0","merchants_number":1,"ean":9781493968381,"category_id":103,"size":null,"min_price":132.5,"low_price_merchant_id":70255345,"ID":4909088,"merchants":["euniverse"],"brand":"undefined","slug":"biomedical-nanotechnology-1","url":"\/unterhaltung\/produkt\/biomedical-nanotechnology-1\/","low_price_merchant_name":"eUniverse"}
{"price-changing":0,"image":"https:\/\/image.vergleiche.ch\/small\/aHR0cHM6Ly9vczEubWVpbmVjbG91ZC5pby9iMTAxNTgvbWVkaWEvaW1hZ2UvY2EvNzIvYWMvNjUwODE2MzYwMDAwMUFfNjAweDYwMC5qcGc=!aHR0cHM6Ly9vczEubWVpbmVjbG91ZC5pby9iMTAxNTgvbWVkaWEvaW1hZ2UvY2EvNzIvYWMvNjUwODE2MzYwMDAwMUFfNjAweDYwMC5qcGd8fnxodHRwczovL2kud2VsdGJpbGQuZGUvcC9wcm9iYWJpbGl0eS1hbmQtc3RhdGlzdGljcy1mb3ItY29tcHV0ZXItc2NpZW5jZS0yNzUwOTE1OTIuanBn","post_title":"Probability and Statistics for Computer Science","deeplink":"https:\/\/cct.connects.ch\/tc.php?t=116298C1969900829T&subid=9783319644097&deepurl=https%3A%2F%2Feuniverse.ch%2Fbuecher%2Fmathematik-naturwissenschaft-technik%2Fmathematik%2F462878%2Fprobability-and-statistics-for-computer-science%3FsPartner%3Dtoppreise","labels":[],"brand_id":1,"post_content":"1 Notation and conventions 9 1.0.1 Background Information........................................................................ 10 1.1 Acknowledgements................................................................................................. 11 I Describing Datasets , 12 2 First Tools for Looking at Data 13 2.1 Datasets....................................................................................................................... 13 2.2 What's Happening? - Plotting Data................................................................. 15 2.2.1 Bar 2.2.2 Histograms................................................................................................... 16 2.2.3 How to Make Histograms...................................................................... 17 2.2.4 Conditional Histograms.......................................................................... 19 2.3 Summarizing 1D Data............................................................................................ 19 2.3.1 The Mean...................................................................................................... 20 2.3.2 Standard Deviation................................................................................... 22 2.3.3 Computing Mean and Standard Deviation Online...................... 26 2.3.4 Variance......................................................................................................... 26 2.3.5 The Median.................................................................................................. 27 2.3.6 Interquartile Range.................................................................................. 29 2.3.7 Using Summaries Sensibly.................................................................... 30 2.4 Plots and Summaries............................................................................................. 31 2.4.1 Some Properties of Histograms.......................................................... 31 2.4.2 Standard Coordinates and Normal Data......................................... 34 2.4.3 Box Plots....................................................................................................... 38 2.5 Whose is bigger? Investigating Australian Pizzas...................................... 39 2.6 You should.................................................................................................................. 43 2.6.1 remember these definitions:................................................................. 43 2.6.2 remember these terms............................................................................ 43 2.6.3 remember these facts:............................................................................. 43 2.6.4 be able to...................................................................................................... 43 3 Looking at Relationships 47 3.1 Plotting 2D Data...................................................................................................... 47 3.1.1 3.1.2 Series.............................................................................................................. 51 3.1.3 Scatter Plots for Spatial Data.............................................................. 53 3.1.4 Exposing Relationships with Scatter Plots..................................... 54 3.2 Correlation.................................................................................................................. 57 3.2.1 The Correlation Coefficient................................................................... 60 3.2.2 Using Correlation to Predict................................................................ 64 3.2.3 Confusion caused by correlation......................................................... 68 1 3.4 You should.................................................................................................................. 72 3.4.1 remember these definitions:................................................................. 72 3.4.2 remember these terms............................................................................ 72 3.4.3 remember these facts: . . . . . 3.4.4 use these procedures: . . . . . . 3.4.5 be able to: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 . . . . . . . . . . . . . . . . . 72 . . . . . . . . . . . . . . . . . 72 II Probability , 78 4 Basic ideas in probability 79 4.1 Experiments, Outcomes and Probability....................................................... 79 4.1.1 Outcomes and Probability...................................................................... 79 4.2 Events........................................................................................................................... 81 4.2.1 Computing Event Probabilities by Counting Outcomes............. 83 4.2.2 The Probability of Events...................................................................... 87 4.2.3 Computing Probabilities by Reasoning about Sets...................... 89 4.3 Independence............................................................................................................ 92 4.3.1 Example: Airline Overbooking............................................................ 96 4.4 Conditional ........................................................ 99 4.4.1 Evaluating Conditional Probabilities.............................................. 100 4.4.2 Detecting Rare Events is Hard......................................................... 104 4.4.3 Conditional Probability and Various Forms of Independence . 106 4.4.4 The Prosecutor's Fallacy 108 4.4.5 Example: The Monty Hall Problem................................................ 110 4.5 Extra Worked Examples.................................................................................... 112 4.5.1 Outcomes and Probability................................................................... 112 4.5.2 Events.......................................................................................................... 114 4.5.3 Independence........................................................................................... 115 4.5.4 Conditional Probability......................................................................... 117 4.6 You should............................................................................................................... 121 4.6.1 remember these definitions:.............................................................. 121 4.6.2 remember these terms......................................................................... 121 4.6.3 remember and use these facts.......................................................... 121 4.6.4 remember these points:....................................................................... 121 4.6.5 be able to.................................................................................................... 121 5 Random Variables and Expectations 128 5.1 Random Variables................................................................................................. 128 5.1.1 Joint and Conditional Probability for Random Variables . . . 131 5.1.2 Just a Little Continuous Probability............................................... 134 5.2 Expectations and Expected Values................................................................ 137 5.2.1 Expected Values...................................................................................... 138 5.2.2 Mean, Variance and Covariance....................................................... 141 5.2.3 Expectations and Statistics................................................................. 145 5.3 The Weak Law of Large Numbers................................................................ 145 5.3.1 IID Samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 5.3.2 Two Inequalities . . . . . . . . . . . . . . . . . . . . . . . .. 146 5.3.3 Proving the Inequalities . . . . . . . . . . . . . . . . . . . . . 147 5.3.4 The Weak Law of Large Numbers.................................................. 149 5.4 Using the Weak Law of Large Numbers 151 5.4.1 Should you accept a bet?..................................................................... 151 5.4.2 Odds, Expectations and Bookmaking - a Cultural Diversion 152 5.4.3 Ending a Game Early 154 5.4.4 Making a Decision with Decision Trees and Expectations . . 154 5.4.5 Utility 156 5.5 You should................................................................................... 159 5.5.1 remember these definitions:.............................................................. 159 5.5.2 remember these terms......................................................................... 159 5.5.3 use and remember these facts.......................................................... 159 5.5.4 be able to.................................................................................................... 160 6 Useful Probability Distributions , 167 6.1 Discrete Distributions 167 6.1.1 The Discrete Uniform Distribution................................................. 167 6.1.2 Bernoulli Random Variables............................................................... 168 6.1.3 The Geometric Distribution................................................................ 168 6.1.4 The Binomial Probability Distribution........................................... 169 6.1.5 Multinomial probabilities..................................................................... 171 6.1.6 The Poisson Distribution..................................................................... 172 6.2 Continuous Distributions , 174 6.2.1 The Continuous Uniform Distribution........................................... 174 6.2.2 The Beta Distribution........................................................................... 174 6.2.3 The Gamma Distribution..................................................................... 176 6.2.4 The Exponential Distribution............................................................ 176 6.3 The Normal Distribution , 178 6.3.1 The Standard Normal Distribution................................................. 178 6.3.2 The Normal Distribution..................................................................... 179 6.3.3 Properties of The Normal Distribution......................................... 180 6.4 Approximating Binomials with Large N 182 6.4.1 Large N....................................................................................................... 183 6.4.2 Getting Normal6.4.3 Using a Normal Approximation to the Binomial Distribution 187 6.5 You should . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.1 remember these definitions: . . . . . . . . . . . . . . . . 6.5.2 remember these terms: . . . . . . . . . . . . . . . . . . . 6.5.3 remember these facts: . . . . . . . . . . . . . . . . . . . 6.5.4 remember these points: . . . . . . . . . . . . . . . . . . . . . 188 . . . 188 . . . 188 . . . 188 . . . 188 III Inference , 196 7 Samples and Populations 197 7.1 The Sample Mean................................................................................................. 197 7.1.1 The Sample Mean is an Estimate of the Population Mean . . 197 7.1.2 The Variance of the Sample Mean.................................................. 198 7.1.3 When The Urn Model Works............................................................ 201 7.1.4 Distributions are Like Populations................................................. 202 7.2 Confidence Intervals............................................................................................ 203 7.2.1 Constructing Confidence Intervals.................................................. 203 7.2.2 Estimating the Variance of the Sample Mean............................ 204 7.2.3 The Probability Distribution of the Sample Mean..................... 206 <,7.2.4 Confidence Intervals for Population Means................................. 208 7.2.5 Standard Error Estimates from Simulation................................. 212 7.3 You should............................................................................................................... 216 7.3.1 remember these definitions:.............................................................. 216 7.3.2 remember these terms......................................................................... 216 7.3.3 remember these facts:........................................................................... 216 7.3.4 use these procedures............................................................................. 216 7.3.5 be able to.................................................................................................... 216 8 The Significance of Evidence 221 8.1 Significance.............................................................................................................. 222 8.1.1 Evaluating Significance......................................................................... 223 8.1.2 P-values....................................................................................................... 225 8.2 Comparing the Mean of Two Populations.................................................. 230 8.2.1 Assuming Known Population Standard Deviations................... 231 8.2.2 Assuming Same, Unknown Population Standard Deviation . 233 8.2.3 Assuming Different, Unknown Population Standard Deviation 235 8.3 Other Useful Tests of Significance................................................................. 237 8.3.1 F-tests and Standard Deviations...................................................... 237 8.3.2 2 Tests of Model Fit............................................................................ 239 8.4 Dangerous Behavior............................................................................................. 244 8.5 You should............................................................................................................... 246 8.5.1 remember these definitions:.............................................................. 246 8.5.2 remember 8.5.3 remember these facts: . . . . . 8.5.4 use these procedures: . . . . . . 8.5.5 be able to: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246 . . . . . . . . . . . . . . . . . 246 . . . . . . . . . . . . . . . . . 246 9 Experiments , 251 9.1 A Simple Experiment: The Effect of a Treatment.................................. 251 9.1.1 Randomized Balanced Experiments............................................... 252 9.1.2 \u00a0 Decomposing Error in Predictions.................................................. 253 9.1.3 Estimating the Noise Variance......................................................... 253 9.1.4 The ANOVA Table.................................................................................. 255 9.1.5 Unbalanced Experiments.................................................................... 257 9.1.6 Significant Differences.......................................................................... 259 9.2 Two Factor Experiments.................................................................................... 261 9.2.1 , Decomposing the Error........................................................................ 264 9.2.2 Interaction Between Effects................................................................ 265 9.2.3 The Effects of a Treatment................................................................. 266 9.2.4 Setting up an ANOVA Table.............................................................. 267 9.3 You should............................................................................................................... 272 9.3.1 remember these definitions:.............................................................. 272 9.3.2 remember these terms......................................................................... 272 9.3.3 remember these facts:........................................................................... 272 9.3.4 use these procedures............................................................................. 272 9.3.5 be able to.................................................................................................... 272 9.3.6 Two-Way Experiments.......................................................................... 274 10 Inferring Probability Models from Data , 275 10.1 Estimating Model Parameters with Maximum Likelihood.................. 275 10.1.1 The Maximum Likelihood Principle............................................... 277 10.1.2 Binomial, Geometric and Multinomial Distributions................ 278 10.1.3 Poisson and Normal Distributions................................................... 281 10.1.4 Confidence Intervals for Model Parameters................................ 286 10.1.5 Cautions about Maximum Likelihood............................................ 288 10.2 Incorporating Priors with Bayesian Inference.......................................... 289 10.2.1 Conjugacy................................................................................................... 292 10.2.2 MAP Inference......................................................................................... 294 10.2.3 Cautions about Bayesian Inference................................................. 296 10.3 Bayesian Inference for Normal Distributions............................................ 296 10.3.1 Example: Measuring Depth of a Borehole................................... 296 10.3.2 Normal Prior and Normal Likelihood Yield Normal Posterior 297 10.3.3 Filtering...................................................................................................... 300 10.4 You should............................................................................................................... 303 10.4.1 remember these definitions:.............................................................. 303 10.4.2 remember these terms......................................................................... 303 10.4.3 remember these facts:........................................................................... 304 10.4.4 use these procedures............................................................................. 304 10.4.5 be able to.................................................................................................... 304 <,IV Tools 312 11 Extracting Important Relationships in High Dimensions 313 11.1 Summaries and Simple Plots........................................................................... 313 11.1.1 The Mean................................................................................................... 314 11.1.2 Stem Plots and Scatterplot Matrices.............................................. 315 11.1.3 Covariance.................................................................................................. 317 11.1.4 The Covariance Matrix......................................................................... 319 11.2 Using Mean and Covariance to Understand High Dimensional Data . 321 11.2.1 Mean and Covariance under Affine Transformations............... 322 11.2.2 . . 324 . . 325 . . 326 . . 327 . . 329 . 332 . . 334 . . 335 . . 335 . . 338 . . 339 . . 341 . . 345 . . 345 . . 345 . . 345 . . 345 . . 345 349 . . 349 . . 350 . . 350 . . 351 . . 351 . . 353 . . 355 . . 357 . . 358 . . 359 . . . 361 Eigenvectors and Diagonalization . . . . . . . . . . . . . . 11.2.3 Diagonalizing Covariance by Rotating Blobs . . . . . . . . 11.2.4 Approximating Blobs . . . . . . . . . . . . . . . . . . . . 11.2.5 Example: Transforming the Height-Weight Blob . . . . . 11.3 Principal Components Analysis . . . . . . . . . . . . . . . . . . . 11.3.1 Example: Representing Colors with Principal Components 11.3.2 Example: Representing Faces with Principal Components 11.4 Multi-Dimensional Scaling . . . . . . . . . . . . . . . . . . . . . . 11.4.1 Choosing Low D Points using High D Distances . . . . . . 11.4.2 Factoring a Dot-Product Matrix . . . . . . . . . . . . . . 11.4.3 Example: Mapping with Multidimensional Scaling . . . . 11.5 Example: Understanding Height and Weight . . . . . . . . . . . 11.6 You should . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.6.1 remember these definitions: . . . . . . . . . . . . . . . . . 11.6.2 remember these terms: . . . . . . . . . . . . . . . . . . . . 11.6.3 remember these facts: . , . . . . . . . . . . . . . . . . . . . 11.6.4 use these procedures: . . . . . . . . . . . . . . . . . . . . . 11.6.5 be able to: . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Learning to Classify 12.1 Classification: The Big Ideas . . . . . . . . . . . . . . . . . . . . 12.1.1 The Error Rate . . . . . . . . . . . . . . . . . . . . . . . . 12.1.2 Overfitting . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1.3 Cross-Validation . . . . . . . . . . . . . . . . . . . . . . . 12.1.4 Is the Classifier Working Well? . . . . . . . . . . . . . . . 12.2 Classifying with Nearest Neighbors . . . . . . . . . . . . . . . . . 12.3 Classifying with Naive Bayes . . . . . . . . . . . . . . . . . . . . 12.3.1 Missing Data . . . . . . . . . . . . . . . . . . . . . . . . . 12.4 The Support 12.4.1 Choosing a Classifier with the Hinge Loss . . . . . . . . . 12.4.2 Finding a Minimum: General Points . . . . . . . . . . . . 12.4.3 Finding a Minimum: Stochastic Gradient Descent . . . . 12.4.4 Example: Training an SVM with Stochastic Gradient Descent 363 12.4.5 Multi-Class Classification with SVMs.............................................. 366 12.5 Classifying with Random Forests................................................................... 367 12.5.1 Building a Decision Tree..................................................................... 367 12.5.2 Choosing a Split with Information Gain........................................ 370 12.5.3 Forests......................................................................................................... 373 12.5.4 Building and Evaluating a Decision Forest.................................. 374 12.5.5 Classifying Data Items with a Decision Forest........................... 375 12.6 You should............................................................................................................... 378 12.6.1 remember these definitions:.............................................................. 378 12.6.2 remember these terms......................................................................... 378 12.6.3 remember these facts:........................................................................... 379 12.6.4 use these procedures............................................................................. 379 12.6.5 be able to.................................................................................................... 379 13.1 The Curse of Dimension..................................................................................... 384 13.1.1 The Curse: Data isn't Where You Think it is............................. 384 13.1.2 Minor Banes of Dimension.................................................................. 386 13.2 The Multivariate Normal Distribution......................................................... 387 13.2.1 Affine Transformations and Gaussians.......................................... 387 13.2.2 Plotting a 2D Gaussian: Covariance Ellipses.............................. 388 13.3 Agglomerative and Divisive Clustering........................................................ 389 13.3.1 Clustering and Distance....................................................................... 391 13.4 , The K-Means Algorithm and Variants......................................................... 392 13.4.1 How to choose K...................................................................................... 395 13.4.2 Soft Assignment....................................................................................... 397 13.4.3 General Comments on K-Means....................................................... 400 13.4.4 K-Mediods.................................................................................................. 400 13.5 Application Example: Clustering Documents........................................... 401 13.5.1 A Topic Model.......................................................................................... 402 13.6 Describing Repetition with Vector Quantization...................................... 403 13.6.1 Vector Quantization............................................................................... 404 13.6.2 Example: Groceries in Portugal....................................................... 406 13.6.3 Efficient Clustering and Hierarchical K Means.......................... 409 13.6.4 Example: Activity from Accelerometer Data............................... 409 13.7 You should............................................................................................................... 413 13.7.1 remember these definitions:.............................................................. 413 13.7.2 remember these terms......................................................................... 413 13.7.3 remember these facts:........................................................................... 413 13.7.4 use these procedures............................................................................. 413 14 Regression , 417 14.1.1 Regression to Make Predictions....................................................... 417 14.1.2 Regression to Spot Trends.................................................................. 419 14.1 Linear Regression and Least Squares.......................................................... 421 14.1.1 Linear Regression................................................................................... 421 14.1.2 Choosing beta.................................................................................................. 422 14.1.3 Solving the Least Squares Problem................................................ 423 14.1.4 , Residuals..................................................................................................... 424 14.1.5 R-squared.................................................................................................... 424 14.2 Producing Good Linear Regressions............................................................. 427 14.2.1 Transforming Variables........................................................................ 428 14.2.2 Problem Data Points have Significant Impact............................ 431 14.2.3 Functions of One Explanatory Variable........................................ 433 14.2.4 Regularizing Linear Regressions...................................................... 435 14.3 , Exploiting Your Neighbors 14.3.1 Using your Neighbors to Predict More than a Number............ 441 14.3.2 Example: Filling Large Holes with Whole Images.................... 441 14.4 You should . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.4.1 remember these definitions: . . . . . . . . . . . . . . 14.4.2 remember these terms: . . . . . . . . . . . . . . . . . . . . . . 444 . . . . . 444 . . . . . 444 14.4.3 remember these facts:........................................................................... 444 14.4.4 remember these procedures:............................................................. 444 15 Markov Chains and Hidden Markov Models , 454 15.1 Markov Chains........................................................................................................ 454 15.1.1 Transition Probability Matrices........................................................ 457 15.1.2 Stationary Distributions....................................................................... 459 15.1.3 Example: Markov Chain Models of Text...................................... 462 15.2 Estimating Properties of Markov Chains.................................................... 465 15.2.1 Simulation.................................................................................................. 465 15.2.2 Simulation Results as Random Variables..................................... 467 15.2.3 Simulating Markov Chains.................................................................. 469 15.3 Example: Ranking the Web by Simulating a Markov Chain................ 472 15.4 Hidden Markov Models and Dynamic Programming............................. 473 15.4.1 Hidden Markov Models........................................................................ 474 15.4.2 Picturing Inference with a Trellis.................................................... 474 15.4.3 Dynamic Programming for HMM's: Formalities....................... 478 15.4.4 , Example: Simple Communication Errors..................................... 478 15.5 You should............................................................................................................... 481 15.5.1 remember these definitions:.............................................................. 481 15.5.2 remember these terms......................................................................... 481 15.5.3 remember these facts:........................................................................... 481 15.5.4 be able to.................................................................................................... 481 V Some Mathematical Background , 484 16 Resources 485 16.1 Useful Material about Matrices....................................................................... 485 16.1.1 The Singular Value Decomposition................................................. 486 16.1.2 Approximating A Symmetric Matrix............................................... 487 16.2 Some Special Functions..................................................................................... 489 16.3 Finding Nearest Neighbors............................................................................... 490 16.4 Entropy and Information Gain........................................................................ 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Probability and Statistics for Computer Sc...
ab CHF 69.90
{"price-changing":0,"image":"https:\/\/image.vergleiche.ch\/small\/aHR0cHM6Ly9vczEubWVpbmVjbG91ZC5pby9iMTAxNTgvbWVkaWEvaW1hZ2UvY2EvNzIvYWMvNjUwODE2MzYwMDAwMUFfNjAweDYwMC5qcGc=!aHR0cHM6Ly9vczEubWVpbmVjbG91ZC5pby9iMTAxNTgvbWVkaWEvaW1hZ2UvY2EvNzIvYWMvNjUwODE2MzYwMDAwMUFfNjAweDYwMC5qcGd8fnxodHRwczovL2kud2VsdGJpbGQuZGUvcC9wcm9iYWJpbGl0eS1hbmQtc3RhdGlzdGljcy1mb3ItY29tcHV0ZXItc2NpZW5jZS0yNzUwOTE1OTIuanBn","post_title":"Probability and Statistics for Computer Science","deeplink":"https:\/\/cct.connects.ch\/tc.php?t=116298C1969900829T&subid=9783319644097&deepurl=https%3A%2F%2Feuniverse.ch%2Fbuecher%2Fmathematik-naturwissenschaft-technik%2Fmathematik%2F462878%2Fprobability-and-statistics-for-computer-science%3FsPartner%3Dtoppreise","labels":[],"brand_id":1,"post_content":"1 Notation and conventions 9 1.0.1 Background Information........................................................................ 10 1.1 Acknowledgements................................................................................................. 11 I Describing Datasets , 12 2 First Tools for Looking at Data 13 2.1 Datasets....................................................................................................................... 13 2.2 What's Happening? - Plotting Data................................................................. 15 2.2.1 Bar 2.2.2 Histograms................................................................................................... 16 2.2.3 How to Make Histograms...................................................................... 17 2.2.4 Conditional Histograms.......................................................................... 19 2.3 Summarizing 1D Data............................................................................................ 19 2.3.1 The Mean...................................................................................................... 20 2.3.2 Standard Deviation................................................................................... 22 2.3.3 Computing Mean and Standard Deviation Online...................... 26 2.3.4 Variance......................................................................................................... 26 2.3.5 The Median.................................................................................................. 27 2.3.6 Interquartile Range.................................................................................. 29 2.3.7 Using Summaries Sensibly.................................................................... 30 2.4 Plots and Summaries............................................................................................. 31 2.4.1 Some Properties of Histograms.......................................................... 31 2.4.2 Standard Coordinates and Normal Data......................................... 34 2.4.3 Box Plots....................................................................................................... 38 2.5 Whose is bigger? Investigating Australian Pizzas...................................... 39 2.6 You should.................................................................................................................. 43 2.6.1 remember these definitions:................................................................. 43 2.6.2 remember these terms............................................................................ 43 2.6.3 remember these facts:............................................................................. 43 2.6.4 be able to...................................................................................................... 43 3 Looking at Relationships 47 3.1 Plotting 2D Data...................................................................................................... 47 3.1.1 3.1.2 Series.............................................................................................................. 51 3.1.3 Scatter Plots for Spatial Data.............................................................. 53 3.1.4 Exposing Relationships with Scatter Plots..................................... 54 3.2 Correlation.................................................................................................................. 57 3.2.1 The Correlation Coefficient................................................................... 60 3.2.2 Using Correlation to Predict................................................................ 64 3.2.3 Confusion caused by correlation......................................................... 68 1 3.4 You should.................................................................................................................. 72 3.4.1 remember these definitions:................................................................. 72 3.4.2 remember these terms............................................................................ 72 3.4.3 remember these facts: . . . . . 3.4.4 use these procedures: . . . . . . 3.4.5 be able to: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 . . . . . . . . . . . . . . . . . 72 . . . . . . . . . . . . . . . . . 72 II Probability , 78 4 Basic ideas in probability 79 4.1 Experiments, Outcomes and Probability....................................................... 79 4.1.1 Outcomes and Probability...................................................................... 79 4.2 Events........................................................................................................................... 81 4.2.1 Computing Event Probabilities by Counting Outcomes............. 83 4.2.2 The Probability of Events...................................................................... 87 4.2.3 Computing Probabilities by Reasoning about Sets...................... 89 4.3 Independence............................................................................................................ 92 4.3.1 Example: Airline Overbooking............................................................ 96 4.4 Conditional ........................................................ 99 4.4.1 Evaluating Conditional Probabilities.............................................. 100 4.4.2 Detecting Rare Events is Hard......................................................... 104 4.4.3 Conditional Probability and Various Forms of Independence . 106 4.4.4 The Prosecutor's Fallacy 108 4.4.5 Example: The Monty Hall Problem................................................ 110 4.5 Extra Worked Examples.................................................................................... 112 4.5.1 Outcomes and Probability................................................................... 112 4.5.2 Events.......................................................................................................... 114 4.5.3 Independence........................................................................................... 115 4.5.4 Conditional Probability......................................................................... 117 4.6 You should............................................................................................................... 121 4.6.1 remember these definitions:.............................................................. 121 4.6.2 remember these terms......................................................................... 121 4.6.3 remember and use these facts.......................................................... 121 4.6.4 remember these points:....................................................................... 121 4.6.5 be able to.................................................................................................... 121 5 Random Variables and Expectations 128 5.1 Random Variables................................................................................................. 128 5.1.1 Joint and Conditional Probability for Random Variables . . . 131 5.1.2 Just a Little Continuous Probability............................................... 134 5.2 Expectations and Expected Values................................................................ 137 5.2.1 Expected Values...................................................................................... 138 5.2.2 Mean, Variance and Covariance....................................................... 141 5.2.3 Expectations and Statistics................................................................. 145 5.3 The Weak Law of Large Numbers................................................................ 145 5.3.1 IID Samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 5.3.2 Two Inequalities . . . . . . . . . . . . . . . . . . . . . . . .. 146 5.3.3 Proving the Inequalities . . . . . . . . . . . . . . . . . . . . . 147 5.3.4 The Weak Law of Large Numbers.................................................. 149 5.4 Using the Weak Law of Large Numbers 151 5.4.1 Should you accept a bet?..................................................................... 151 5.4.2 Odds, Expectations and Bookmaking - a Cultural Diversion 152 5.4.3 Ending a Game Early 154 5.4.4 Making a Decision with Decision Trees and Expectations . . 154 5.4.5 Utility 156 5.5 You should................................................................................... 159 5.5.1 remember these definitions:.............................................................. 159 5.5.2 remember these terms......................................................................... 159 5.5.3 use and remember these facts.......................................................... 159 5.5.4 be able to.................................................................................................... 160 6 Useful Probability Distributions , 167 6.1 Discrete Distributions 167 6.1.1 The Discrete Uniform Distribution................................................. 167 6.1.2 Bernoulli Random Variables............................................................... 168 6.1.3 The Geometric Distribution................................................................ 168 6.1.4 The Binomial Probability Distribution........................................... 169 6.1.5 Multinomial probabilities..................................................................... 171 6.1.6 The Poisson Distribution..................................................................... 172 6.2 Continuous Distributions , 174 6.2.1 The Continuous Uniform Distribution........................................... 174 6.2.2 The Beta Distribution........................................................................... 174 6.2.3 The Gamma Distribution..................................................................... 176 6.2.4 The Exponential Distribution............................................................ 176 6.3 The Normal Distribution , 178 6.3.1 The Standard Normal Distribution................................................. 178 6.3.2 The Normal Distribution..................................................................... 179 6.3.3 Properties of The Normal Distribution......................................... 180 6.4 Approximating Binomials with Large N 182 6.4.1 Large N....................................................................................................... 183 6.4.2 Getting Normal6.4.3 Using a Normal Approximation to the Binomial Distribution 187 6.5 You should . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.1 remember these definitions: . . . . . . . . . . . . . . . . 6.5.2 remember these terms: . . . . . . . . . . . . . . . . . . . 6.5.3 remember these facts: . . . . . . . . . . . . . . . . . . . 6.5.4 remember these points: . . . . . . . . . . . . . . . . . . . . . 188 . . . 188 . . . 188 . . . 188 . . . 188 III Inference , 196 7 Samples and Populations 197 7.1 The Sample Mean................................................................................................. 197 7.1.1 The Sample Mean is an Estimate of the Population Mean . . 197 7.1.2 The Variance of the Sample Mean.................................................. 198 7.1.3 When The Urn Model Works............................................................ 201 7.1.4 Distributions are Like Populations................................................. 202 7.2 Confidence Intervals............................................................................................ 203 7.2.1 Constructing Confidence Intervals.................................................. 203 7.2.2 Estimating the Variance of the Sample Mean............................ 204 7.2.3 The Probability Distribution of the Sample Mean..................... 206 <,7.2.4 Confidence Intervals for Population Means................................. 208 7.2.5 Standard Error Estimates from Simulation................................. 212 7.3 You should............................................................................................................... 216 7.3.1 remember these definitions:.............................................................. 216 7.3.2 remember these terms......................................................................... 216 7.3.3 remember these facts:........................................................................... 216 7.3.4 use these procedures............................................................................. 216 7.3.5 be able to.................................................................................................... 216 8 The Significance of Evidence 221 8.1 Significance.............................................................................................................. 222 8.1.1 Evaluating Significance......................................................................... 223 8.1.2 P-values....................................................................................................... 225 8.2 Comparing the Mean of Two Populations.................................................. 230 8.2.1 Assuming Known Population Standard Deviations................... 231 8.2.2 Assuming Same, Unknown Population Standard Deviation . 233 8.2.3 Assuming Different, Unknown Population Standard Deviation 235 8.3 Other Useful Tests of Significance................................................................. 237 8.3.1 F-tests and Standard Deviations...................................................... 237 8.3.2 2 Tests of Model Fit............................................................................ 239 8.4 Dangerous Behavior............................................................................................. 244 8.5 You should............................................................................................................... 246 8.5.1 remember these definitions:.............................................................. 246 8.5.2 remember 8.5.3 remember these facts: . . . . . 8.5.4 use these procedures: . . . . . . 8.5.5 be able to: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246 . . . . . . . . . . . . . . . . . 246 . . . . . . . . . . . . . . . . . 246 9 Experiments , 251 9.1 A Simple Experiment: The Effect of a Treatment.................................. 251 9.1.1 Randomized Balanced Experiments............................................... 252 9.1.2 \u00a0 Decomposing Error in Predictions.................................................. 253 9.1.3 Estimating the Noise Variance......................................................... 253 9.1.4 The ANOVA Table.................................................................................. 255 9.1.5 Unbalanced Experiments.................................................................... 257 9.1.6 Significant Differences.......................................................................... 259 9.2 Two Factor Experiments.................................................................................... 261 9.2.1 , Decomposing the Error........................................................................ 264 9.2.2 Interaction Between Effects................................................................ 265 9.2.3 The Effects of a Treatment................................................................. 266 9.2.4 Setting up an ANOVA Table.............................................................. 267 9.3 You should............................................................................................................... 272 9.3.1 remember these definitions:.............................................................. 272 9.3.2 remember these terms......................................................................... 272 9.3.3 remember these facts:........................................................................... 272 9.3.4 use these procedures............................................................................. 272 9.3.5 be able to.................................................................................................... 272 9.3.6 Two-Way Experiments.......................................................................... 274 10 Inferring Probability Models from Data , 275 10.1 Estimating Model Parameters with Maximum Likelihood.................. 275 10.1.1 The Maximum Likelihood Principle............................................... 277 10.1.2 Binomial, Geometric and Multinomial Distributions................ 278 10.1.3 Poisson and Normal Distributions................................................... 281 10.1.4 Confidence Intervals for Model Parameters................................ 286 10.1.5 Cautions about Maximum Likelihood............................................ 288 10.2 Incorporating Priors with Bayesian Inference.......................................... 289 10.2.1 Conjugacy................................................................................................... 292 10.2.2 MAP Inference......................................................................................... 294 10.2.3 Cautions about Bayesian Inference................................................. 296 10.3 Bayesian Inference for Normal Distributions............................................ 296 10.3.1 Example: Measuring Depth of a Borehole................................... 296 10.3.2 Normal Prior and Normal Likelihood Yield Normal Posterior 297 10.3.3 Filtering...................................................................................................... 300 10.4 You should............................................................................................................... 303 10.4.1 remember these definitions:.............................................................. 303 10.4.2 remember these terms......................................................................... 303 10.4.3 remember these facts:........................................................................... 304 10.4.4 use these procedures............................................................................. 304 10.4.5 be able to.................................................................................................... 304 <,IV Tools 312 11 Extracting Important Relationships in High Dimensions 313 11.1 Summaries and Simple Plots........................................................................... 313 11.1.1 The Mean................................................................................................... 314 11.1.2 Stem Plots and Scatterplot Matrices.............................................. 315 11.1.3 Covariance.................................................................................................. 317 11.1.4 The Covariance Matrix......................................................................... 319 11.2 Using Mean and Covariance to Understand High Dimensional Data . 321 11.2.1 Mean and Covariance under Affine Transformations............... 322 11.2.2 . . 324 . . 325 . . 326 . . 327 . . 329 . 332 . . 334 . . 335 . . 335 . . 338 . . 339 . . 341 . . 345 . . 345 . . 345 . . 345 . . 345 . . 345 349 . . 349 . . 350 . . 350 . . 351 . . 351 . . 353 . . 355 . . 357 . . 358 . . 359 . . . 361 Eigenvectors and Diagonalization . . . . . . . . . . . . . . 11.2.3 Diagonalizing Covariance by Rotating Blobs . . . . . . . . 11.2.4 Approximating Blobs . . . . . . . . . . . . . . . . . . . . 11.2.5 Example: Transforming the Height-Weight Blob . . . . . 11.3 Principal Components Analysis . . . . . . . . . . . . . . . . . . . 11.3.1 Example: Representing Colors with Principal Components 11.3.2 Example: Representing Faces with Principal Components 11.4 Multi-Dimensional Scaling . . . . . . . . . . . . . . . . . . . . . . 11.4.1 Choosing Low D Points using High D Distances . . . . . . 11.4.2 Factoring a Dot-Product Matrix . . . . . . . . . . . . . . 11.4.3 Example: Mapping with Multidimensional Scaling . . . . 11.5 Example: Understanding Height and Weight . . . . . . . . . . . 11.6 You should . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.6.1 remember these definitions: . . . . . . . . . . . . . . . . . 11.6.2 remember these terms: . . . . . . . . . . . . . . . . . . . . 11.6.3 remember these facts: . , . . . . . . . . . . . . . . . . . . . 11.6.4 use these procedures: . . . . . . . . . . . . . . . . . . . . . 11.6.5 be able to: . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Learning to Classify 12.1 Classification: The Big Ideas . . . . . . . . . . . . . . . . . . . . 12.1.1 The Error Rate . . . . . . . . . . . . . . . . . . . . . . . . 12.1.2 Overfitting . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1.3 Cross-Validation . . . . . . . . . . . . . . . . . . . . . . . 12.1.4 Is the Classifier Working Well? . . . . . . . . . . . . . . . 12.2 Classifying with Nearest Neighbors . . . . . . . . . . . . . . . . . 12.3 Classifying with Naive Bayes . . . . . . . . . . . . . . . . . . . . 12.3.1 Missing Data . . . . . . . . . . . . . . . . . . . . . . . . . 12.4 The Support 12.4.1 Choosing a Classifier with the Hinge Loss . . . . . . . . . 12.4.2 Finding a Minimum: General Points . . . . . . . . . . . . 12.4.3 Finding a Minimum: Stochastic Gradient Descent . . . . 12.4.4 Example: Training an SVM with Stochastic Gradient Descent 363 12.4.5 Multi-Class Classification with SVMs.............................................. 366 12.5 Classifying with Random Forests................................................................... 367 12.5.1 Building a Decision Tree..................................................................... 367 12.5.2 Choosing a Split with Information Gain........................................ 370 12.5.3 Forests......................................................................................................... 373 12.5.4 Building and Evaluating a Decision Forest.................................. 374 12.5.5 Classifying Data Items with a Decision Forest........................... 375 12.6 You should............................................................................................................... 378 12.6.1 remember these definitions:.............................................................. 378 12.6.2 remember these terms......................................................................... 378 12.6.3 remember these facts:........................................................................... 379 12.6.4 use these procedures............................................................................. 379 12.6.5 be able to.................................................................................................... 379 13.1 The Curse of Dimension..................................................................................... 384 13.1.1 The Curse: Data isn't Where You Think it is............................. 384 13.1.2 Minor Banes of Dimension.................................................................. 386 13.2 The Multivariate Normal Distribution......................................................... 387 13.2.1 Affine Transformations and Gaussians.......................................... 387 13.2.2 Plotting a 2D Gaussian: Covariance Ellipses.............................. 388 13.3 Agglomerative and Divisive Clustering........................................................ 389 13.3.1 Clustering and Distance....................................................................... 391 13.4 , The K-Means Algorithm and Variants......................................................... 392 13.4.1 How to choose K...................................................................................... 395 13.4.2 Soft Assignment....................................................................................... 397 13.4.3 General Comments on K-Means....................................................... 400 13.4.4 K-Mediods.................................................................................................. 400 13.5 Application Example: Clustering Documents........................................... 401 13.5.1 A Topic Model.......................................................................................... 402 13.6 Describing Repetition with Vector Quantization...................................... 403 13.6.1 Vector Quantization............................................................................... 404 13.6.2 Example: Groceries in Portugal....................................................... 406 13.6.3 Efficient Clustering and Hierarchical K Means.......................... 409 13.6.4 Example: Activity from Accelerometer Data............................... 409 13.7 You should............................................................................................................... 413 13.7.1 remember these definitions:.............................................................. 413 13.7.2 remember these terms......................................................................... 413 13.7.3 remember these facts:........................................................................... 413 13.7.4 use these procedures............................................................................. 413 14 Regression , 417 14.1.1 Regression to Make Predictions....................................................... 417 14.1.2 Regression to Spot Trends.................................................................. 419 14.1 Linear Regression and Least Squares.......................................................... 421 14.1.1 Linear Regression................................................................................... 421 14.1.2 Choosing beta.................................................................................................. 422 14.1.3 Solving the Least Squares Problem................................................ 423 14.1.4 , Residuals..................................................................................................... 424 14.1.5 R-squared.................................................................................................... 424 14.2 Producing Good Linear Regressions............................................................. 427 14.2.1 Transforming Variables........................................................................ 428 14.2.2 Problem Data Points have Significant Impact............................ 431 14.2.3 Functions of One Explanatory Variable........................................ 433 14.2.4 Regularizing Linear Regressions...................................................... 435 14.3 , Exploiting Your Neighbors 14.3.1 Using your Neighbors to Predict More than a Number............ 441 14.3.2 Example: Filling Large Holes with Whole Images.................... 441 14.4 You should . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.4.1 remember these definitions: . . . . . . . . . . . . . . 14.4.2 remember these terms: . . . . . . . . . . . . . . . . . . . . . . 444 . . . . . 444 . . . . . 444 14.4.3 remember these facts:........................................................................... 444 14.4.4 remember these procedures:............................................................. 444 15 Markov Chains and Hidden Markov Models , 454 15.1 Markov Chains........................................................................................................ 454 15.1.1 Transition Probability Matrices........................................................ 457 15.1.2 Stationary Distributions....................................................................... 459 15.1.3 Example: Markov Chain Models of Text...................................... 462 15.2 Estimating Properties of Markov Chains.................................................... 465 15.2.1 Simulation.................................................................................................. 465 15.2.2 Simulation Results as Random Variables..................................... 467 15.2.3 Simulating Markov Chains.................................................................. 469 15.3 Example: Ranking the Web by Simulating a Markov Chain................ 472 15.4 Hidden Markov Models and Dynamic Programming............................. 473 15.4.1 Hidden Markov Models........................................................................ 474 15.4.2 Picturing Inference with a Trellis.................................................... 474 15.4.3 Dynamic Programming for HMM's: Formalities....................... 478 15.4.4 , Example: Simple Communication Errors..................................... 478 15.5 You should............................................................................................................... 481 15.5.1 remember these definitions:.............................................................. 481 15.5.2 remember these terms......................................................................... 481 15.5.3 remember these facts:........................................................................... 481 15.5.4 be able to.................................................................................................... 481 V Some Mathematical Background , 484 16 Resources 485 16.1 Useful Material about Matrices....................................................................... 485 16.1.1 The Singular Value Decomposition................................................. 486 16.1.2 Approximating A Symmetric Matrix............................................... 487 16.2 Some Special Functions..................................................................................... 489 16.3 Finding Nearest Neighbors............................................................................... 490 16.4 Entropy and Information Gain........................................................................ 493","merchants_number":2,"ean":9783319644097,"category_id":103,"size":null,"min_price":69.900000000000005684341886080801486968994140625,"low_price_merchant_id":70255345,"ID":5051447,"merchants":["euniverse","weltbild"],"brand":"undefined","slug":"probability-and-statistics-for-computer-science","url":"\/unterhaltung\/produkt\/probability-and-statistics-for-computer-science\/","low_price_merchant_name":"eUniverse"}
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Tolix
Tolix Tabouret Ha
CHF 465.00
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{"price-changing":0.057306590257879659600082078441118937917053699493408203125,"image":"https:\/\/image.vergleiche.ch\/small\/aHR0cHM6Ly9hc3NldHMudGhhbGlhLm1lZGlhL2ltZy9hcnRpa2VsL2NjMDdkNDI3YjM5Yjg5YjBiM2NhYWNiMDc0YjUyZTcyM2FkYTk3ZTgtMDAtMDAuanBlZw==!aHR0cHM6Ly9hc3NldHMudGhhbGlhLm1lZGlhL2ltZy9hcnRpa2VsL2NjMDdkNDI3YjM5Yjg5YjBiM2NhYWNiMDc0YjUyZTcyM2FkYTk3ZTgtMDAtMDAuanBlZw==","post_title":"M: A 24 hour cookbook","deeplink":"https:\/\/www.awin1.com\/pclick.php?p=25177700741&a=401125&m=13971&pref1=9781472938541","labels":[],"brand_id":35514,"post_content":"Winner of the Open Table Diner's Choice award for 2015, M is two restaurants in one. With RAW and GRILL side by side, and open from early morning until midnight every day, M venues offers diners endless opportunities, and this exciting new cookbook presents them both. With RAW, M is informal and high energy, delighting patrons with small dishes and sharing plates of tartars, tiraditos and sashimi, while GRILL specialises in the best steaks from around the world. Alongside this, the M-Bar offers expert wines, which can be bought via the M Wine Store and online, and there is a secret 'den', making both M restaurants a multi-purpose hotspot for Londoners. Innovative and much loved by its patrons, M even offers pampered pooch parties, including a doggie dance off, for those who love the restaurant's incredible food - and their pets. With essays and recipes covering a full 24 hours in these iconic London restaurants, M: A 24 Hour Cookbook showcases the very best the restaurant has to offer, with stunning new photography of the recipes and the restaurants by Jodi Hinds.","merchants_number":1,"ean":9781472938541,"category_id":1,"size":null,"min_price":32.89999999999999857891452847979962825775146484375,"low_price_merchant_id":70254503,"ID":10074796,"merchants":["orell-fuessli"],"brand":"Bloomsbury USA","slug":"m-a-24-hour-cookbook","url":"\/produkt\/m-a-24-hour-cookbook\/","low_price_merchant_name":"Orell F\u00fcssli"}
Bloomsbury USA
M: A 24 hour cookbook
CHF 32.90
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{"price-changing":0,"image":"https:\/\/image.vergleiche.ch\/small\/aHR0cHM6Ly9pLndlbHRiaWxkLmRlL3AvYmlvbWVkaWNhbC1uYW5vdGVjaG5vbG9neS0zMTI1NzMzNzQuanBn!aHR0cHM6Ly9pLndlbHRiaWxkLmRlL3AvYmlvbWVkaWNhbC1uYW5vdGVjaG5vbG9neS0zMTI1NzMzNzQuanBnfH58aHR0cHM6Ly9vczEubWVpbmVjbG91ZC5pby9iMTAxNTgvbWVkaWEvaW1hZ2UvZGMvNWEvMGUvNzI5NTE3ODcwMDAwMUFfNjAweDYwMC5qcGc=","post_title":"Biomedical Nanotechnology: Methods and Protocols","deeplink":"https:\/\/track.adtraction.com\/t\/t?a=1632201226&as=1592767275&t=2&tk=1&url=https:\/\/www.weltbild.ch\/artikel\/x\/_34534562-1","labels":[],"brand_id":438434,"post_content":"Preface...Table of Contents...Contributing Authors...1.\u00a0Quantification of siRNA Duplexes Bound to Gold Nanoparticle SurfacesJilian R. Melamed, Rachel S. Riley, Danielle M. Valcourt, Margaret M. Billingsley, Nicole L. Kreuzberger, and Emily S. Day2.\u00a0Ligand Exchange and 1H NMR Quantification of Single- and Mixed-Moiety Thiolated Ligand Shells on Gold NanoparticlesAshley M. Smith and Jill E. Millstone3.\u00a0Nanoparticle Tracking Analysis for Determination of Hydrodynamic Diameter, Concentration, and Zeta Potential of Polyplex NanoparticlesDavid R. Wilson and Jordan J. Green4.\u00a0Magnetic Characterization of Iron Oxide Nanoparticles for Biomedical ApplicationsLorena Maldonado-Camargo, Mythreyi Unni, and Carlos Rinaldi5.\u00a0Preparation of Magnetic Nanoparticles for Biomedical ApplicationsXiaolian Sun and Shouheng Sun6.\u00a0Brain-Penetrating Nanoparticles for Analysis of the Brain MicroenvironmentElizabeth Nance\u00a07.\u00a0Volumetric Bar-Chart Chips for BiosensingYujun Song, Ying Li, and Lidong Qin8.\u00a0qFlow Cytometry-Based Receptoromic Screening: A High-Throughput Quantification Approach Informing Biomarker Selection and Nanosensor DevelopmentSi Chen, Jared Weddell, Pavan Gupta, Grace Conard, James Parkin, and Princess I. Imoukhuede9.\u00a0Evaluating Nanoparticle Binding to Blood Compartment Immune Cells in High-Throughput with Flow CytometryShann S. Yu10.\u00a0A Gold@Polydopamine Core-Shell Nanoprobe for Long-Term Intracellular Detection of MicroRNAs in Differentiating Stem CellsChun Kit K. Choi, Chung Hang J. Choi, and Liming Bian11.\u00a0Antibody-Conjugated Single Quantum Dot Tracking of Membrane Neurotransmitter Transporters in Primary Neuronal CulturesDanielle M. Bailey, Oleg Kovtuna, and Sandra J. Rosenthal12.\u00a0Spectroscopic Photoacoustic Imaging of Gold NanorodsAustin Van Namen and Geoffrey P. Luke13.\u00a0Dual Wavelength-Triggered Gold Nanorods for Anti-Cancer TreatmentDennis B. Pacardo, Frances S. Ligler, and Zhen Gu14.\u00a0Photolabile Self-Immolative DNA-Drug NanostructuresXuyu Tan and Ke Zhang15.\u00a0Enzyme-Responsive Nanoparticles for the Treatment of DiseaseCassandra E. Callmann and Nathan C. Gianneschi16.\u00a0NanoScript: A Versatile Nanoparticle-Based Synthetic Transcription Factor for Innovative Gene ManipulationKholud Dardir, Christopher Rathnam, and KiBum Lee17.\u00a0Glucose-Responsive Insulin Delivery by Microneedle-Array Patches Loaded with Hypoxia-Sensitive VesiclesJicheng Yu, Yuqi Zhang, and Zhen Gu18.\u00a0Electrospun Nanofiber Scaffolds and their Hydrogel Composites for the Engineering and Regeneration of Soft TissuesOhan S. Manoukian, Rita Matta, Justin Letendre, Paige Collins, Augustus D. Mazzocca, and Sangamesh G. Kumbar19.\u00a0Application of Hydrogel Template Strategy in Ocular Drug DeliveryCrystal S. Shin, Daniela C. Marcano, Kinam Park, and Ghanashyam Acharya20.\u00a0High-Accuracy Determination of Cytotoxic Responses from Graphene Oxide Exposure using Imaging Flow CytometrySandra Vranic and Kostas Kostarelos21.\u00a0Air-Liquid Interface Cell Exposures to Nanoparticle AerosolsNastassja A. Lewinski, Nathan J. Liu, Akrivi Asimakopoulou, Eleni Papaioannou, Athanasios Konstandopoulos, and Michael Riediker22.\u00a0Returning to the Patent Landscapes for Nanotechnology: Assessing the Garden That It Has Grown IntoDiana M. Bowman, Douglas J. Sylvester, and Anthony D. Marino\u00a0","merchants_number":2,"ean":9781493983148,"category_id":1,"size":null,"min_price":132,"low_price_merchant_id":27291482,"ID":19693904,"merchants":["weltbild","euniverse"],"brand":"Springer Berlin,Humana","slug":"biomedical-nanotechnology-methods-and-protocols-1","url":"\/produkt\/biomedical-nanotechnology-methods-and-protocols-1\/","low_price_merchant_name":"Weltbild"}
Springer Berlin,Humana
Biomedical Nanotechnology: Methods and Pro...
ab CHF 132.00
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{"price-changing":0,"image":"https:\/\/image.vergleiche.ch\/small\/aHR0cHM6Ly9vczEubWVpbmVjbG91ZC5pby9iMTAxNTgvbWVkaWEvaW1hZ2UvZDQvZWQvYTgvMTU1NDYyODIwMDAwMUFfNjAweDYwMC5qcGc=!aHR0cHM6Ly9vczEubWVpbmVjbG91ZC5pby9iMTAxNTgvbWVkaWEvaW1hZ2UvZDQvZWQvYTgvMTU1NDYyODIwMDAwMUFfNjAweDYwMC5qcGc=","post_title":"Computational Methods, w. CD-ROM. Pt.1","deeplink":"https:\/\/cct.connects.ch\/tc.php?t=116298C1969900829T&subid=9781402039522&deepurl=https%3A%2F%2Feuniverse.ch%2Fbuecher%2Fmathematik-naturwissenschaft-technik%2Ftechnik%2F419296%2Fcomputational-methods-w.-cd-rom.-pt.1%3FsPartner%3Dtoppreise","labels":[],"brand_id":436598,"post_content":"TOPOLOGICAL OPTIMIZATION OF CONTINUUM STRUCTURE WITH GLOBAL STRESS CONSTRAINTS BASED ON ICM METHOD.- TOPOLOGICAL OPTIMIZATION OF FRAME STRUCTURES UNDER MULTIPLE LOADING CASES$^*$.- OPTIMAL DISPLACEMENT CONTROL SIMULATION OF ELECTRIC-MECHANICAL COUPLED TRUSSES.- PROTEIN SECONDARY STRUCTURE PREDICTION METHODS BASED ONRBF NEURAL NETWORKS.- A HYBRID META-HEURISTIC FOR A ROUTING PROBLEM.- IDENTIFICATION OF GEOMETRIC PARAMETERS OF DRAWBEAD USING NEURAL NETWORKS.- IMPROVED IMMUNE GENETIC ALGORITHM FOR SOLVING FLOW SHOP SCHEDULING PROBLEM.- A DISCRETE PARTICLE SWARM OPTIMIZATION ALGORITHM FOR TRAVELLING SALESMAN PROBLEM.- A SEARCH METHOD FOR FINDING A SIMPLE NASH EQUILIBRIUM.- A HERO EVOLUTIONARY ALGORITHM HYBRIDIZING FROM PSO AND GA.- A DATA COLLECTION MODEL FOR INTRUSION DETECTION SYSTEM BASED ON SIMPLE RANDOM SAMPLING.- DESTRUCTIVE EXTENSION RULE IN PROPOSITION MODAL LOGIC K.- A NOVEL PARTICLE SWARM OPTIMIZATION-BASED APPROACH FOR JOB-SHOP SCHEDULING.- UNCALIBRATED ROBOTIC ARM VISUAL SERVO CONTROL.- INTERLINEATION AND INTERFLATION FUNCTIONS OF MANY VARIABLES (BLENDING FUNCTION INTERPOLATION) AND ECONOMICAL ALGORITHMS IN THE APPROXIMATION THEORY.- INDEPENDENT COMPONENT ANALYSIS OF DYNAMIC CONTRAST-ENHANCED IMAGES: THE NUMBER OF COMPONENTS.- THE GEOMETRIC CONSTRAINT SOLVING BASED ON HYBRID GENETIC ALGORITHM OF CONJUGATE GRADIENT.- FAST IMAGE MOSAICS ALGORITHM USING PARTICLE SWARM OPTIMIZATION.- CHECKING CONSISTENCY IN HYBRID QUALITATIVE SPATIAL REASONING.- ICA-SCS DENOISING METHOD FOR WATERMARKING SCHEME.- STUDY AND IMPLEMENTATION OF SIMPLIFIED WWW DATA MODEL IN USE FOR WEB MINING.- AN EXTENSION OF EARLEY'S ALGORITHM FOR EXTENDED GRAMMARS.- THE RESEARCH ON POLICY-BASED REGISTRATION MECHANISM OF MOBILE TERMINALS IN MOBILE IP NETWORK.- OPERATIONAL SEMANTIC TO THE EXECUTION OF THE PROCESS MODEL.- FUZZY CLUSTERING ON THE WEB IMPLEMENTED BY JSP TECHNOLOGY.- TEXT CLASSIFICATION FOR CHINESE WEB DOCUMENTS.- LOGIC-BASED CONSTRAINT HANDLING IN RESOURCE-CONSTRAINED SCHEDULING PROBLEMS.- PARTICLE SWARM OPTIMIZATION METHOD USED IN PIXEL-BASED TEXTURE SYNTHESIS.- FORMAL METHOD IN IMPLEMENTATION OF ATLAS LANGUAGE*.- GENETIC ALGORITHM FOR EVALUATION METRICS IN TOPICAL WEB CRAWLING.- A BOUNDARY METHOD TO SPEED UP TRAINING SUPPORT VECTOR MACHINES.- A FAST ALGORITHM FOR GENERATING CONCEPTS USING AN ATTRIBUTE TABLE.- ALGORITHM OF MEASUREMENT-BASED ADMISSION CONTROL FOR GPRS.- SURFACE RECOGNITION OF AUTOMOBILE PANEL BASED ON SECTION CURVE IDENTIFICATION.- DYNAMIC CLUSTERING ALGORITHM BASED ON ADAPTIVE RESONANCE THEORY.- ONTOLOGY LEARNING USING WORDNET LEXICON.- GENETIC PROGRAMMING FOR MAXIMUM-LIKELIHOOD PHYLOGENY INFERENCE.- MINING DOMINANCE ASSOCIATION RULES IN PREFERENCE-ORDERED DATA.- Mining Ordinal Patterns For Data Cleaning.- USER ASSOCIATION MINING BASED ON CONCEPT LATTICE.- A PLM-ORIENTED WORKFLOW MODEL.- A LANGUAGE FOR SPECIFYING CONSTRAINTS IN WFMSs.- ONTOLOGY BASED WORKFLOW MODEL.- CONSTRAINED MULTI-SAMPLE TEXTURE SYNTHESIS.- A GENERAL INCREMENTAL HIERARCHICAL CLUSTERING METHOD.- ACTIVATING IRREGULAR DIMENSIONS IN OLAP.- A NEW COMPUTATIONAL METHOD OF INTERSECTION FOR RAY TRACING.- STUDY ON PARTNERS SELECTION OF AGILE VIRTUAL ENTERPRISE BASED ON PARTICLE SWARM OPTIMIZATION.- ADAPTIVE DIRECTIONAL WEIGHTED MEDIAN FILTERING.- AN INTERACTIVE WORKFLOW MANAGEMENT FUNCTION MODEL.- PACKET FAIR SCHEDULING ALGORITHM BASED ON WEIGHTS DYNAMIC COMPENSATION.- EXTENDED ADAPTIVE WEIGHTED AVERAGING FILTER MODEL.- AN IMPROVED QUANTUM-INSPIRED EVOLUTIONARY ALGORITHM FOR CLUSTERING GENE EXPRESSION DATA.- AUTOMATIC BUFFER OVERFLOW DETECTION BASED ON OPERATION SEMANTIC.- QUANTUM-INSPIRED EVOLUTIONARY ALGORITHM FOR TRAVELLING SALESMAN PROBLEM.- AN IMPROVED COEVOLUTION ALGORITHM FOR FUZZY MODELLING.- A COMPUTATIONAL METHOD FOR SOLVING CAUCHY PROBLEMS OF ELLIPTIC OPERATORS.- NUMERICAL DETERMINATION OF THE RESONANCE FREQUENCIES AND EIGENMODES USING THE MFS.- SCATTERED NODE COMPACT FINITE DIFFERENCE-TYPE FORMULAS GENERATED FROM RADIAL BASIS FUNCTIONS.- EXPLOSION SIMULATION BY SMOOTHED PARTICLE HYDRODYNAMICS.- CLASSIC TAYLOR-BAR IMPACT TEST REVISITED USING 3D SPH.- A MESH FREE METHOD BASED ON AN OPTIMIZATION TECHNIQUE AND THE MOVING LEAST SQUARES APPROXIMATION.- FREE VIBRATION ANALYSIS OF TIMOSHENKO BEAMS BY RADIAL BASIS FUNCTIONS.- MOVING LEAST SQUARE SPH USING FIXED KERNEL FOR LARGE DEFORMATION ELASTO-PLASTIC ANALYSIS.- A COUPLED MESHFREE\/SCALED BOUNDARY METHOD.- BASIC DISCUSSION OF BOUNDARY CONDITION OF SMOOTHED PARTICLE HYDRODYNAMICS FOR ANALYSIS OF CEREBRAL CONTUSION.- DEPOSITION OF COLLOIDAL PARTICLES FROM PRESSURE DRIVEN MICROFLUIDIC FLOW-BROWNIAN DYNAMICS SIMULATION.- S SHAPE PARAMETERS OF MULTIQUADRICS IN THE HEAVISIDE WEIGHTED MLPG METHOD.- APPLICATION OF HIGH ORDER BASIS FUNCTIONS IN SOLID MECHANICS BY ELEMENT FREE GALERKIN (EFG) METHOD.- AN ADAPTIVE MESHFREE COLLOCATION METHOD FOR STATIC AND DYNAMIC NONLINEAR PROBLEMS.- MESHLESS NATURAL NEIGHBOUR METHOD AND ITS APPLICATION IN ELASTO-PLASTIC PROBLEMS.- A MESHLESS LOCAL PETROV-GALERKIN METHOD FOR ELASTO-PLASTIC PROBLEMS.- PARTICLE-PARTITION OF UNITY METHODS IN ELASTICITY.- A MESHFREE APPROXIMATION WITH ALLMAN'S ROTATIONAL DOFS.- THE VORTEX METHOD APPLIED TO SIMULATION OF HOMOGENEOUS ISOTROPIC TURBULENCE.- AN APPLICATION OF THE LOCAL PETROV-GALERKIN METHOD IN SOLVING GEOMETRICALLY NONLINEAR PROBLEMS.- ELASTIC-PLASTIC LARGE DEFORMATION ANALYSIS USING SPH.- TWO ALGORITHMS FOR SUPERCONVERGENT STRESS RECOVERY BASED ON MLS AND FINITE POINTS METHOD.- GEOMETRICALLY NONLINEAR ANALYSIS USING MESHFREE RPIM.- GALERKIN MESHLESS METHODS BASED ON PARTITION OF UNITY QUADRATURE.- RADIAL POINT INTERPOLATION COLLOCATION METHOD (RPICM) USING UPWIND BIASED LOCAL SUPPORT SCHEME FOR SOLVING CONVECTION-DOMINATED EQUATIONS.- MESHFREE NUMERICAL SOLUTION OF TWO-PHASE FLOW THROUGH POROUS MEDIA.- STRESS ANALYSIS OF 3-D SOLIDS USING A MESHFREE RADIAL POINT INTERPOLATION METHOD.- 3-D HEAT TRANSFER ANALYSIS USING A COLLOCATION METHOD TOGETHER WITH RPIM SHAPE FUNCTIONS AND FIC BOUNDARY CONDITIONS.- SYMPLECTIC ANALYSIS FOR OPTICAL WAVEGUIDES IN LAYERED MEDIA.- A COMPUTATIONAL ANALYSIS OF THERMAL RESIDUAL STRESS DURING MAGNETIC QUENCHING.- PARALLEL FEM ANALYSIS OF HIGH FREQUENCY ELECTROMAGNETIC WAVE IN AN ENVIRONMENT.- AND Ta ATOMS IN SILK-LIKE AMORPHOUS POLYMER.- MULTISCALE COUPLING OF MESHLESS METHOD AND MOLECULAR DYNAMICS.- MECHANISMS OF DISINTEGRATION OF MINERAL MEDIA EXPOSED TO HIGH-POWER ELECTROMAGNETIC PULSES.- DOMAIN SWITCHING CRITERIA FOR TETRAGONAL PHASE FERROELECTRICS: A COMPARATIVE STUDY.- GENERALIZED MAGNETO-THERMOELASTICITY SOLVED BY FEM IN TIME DOMAIN.- MICROSTRUCTURE REPRESENTATION AND SIMULATION TOOLS FOR MICROSTRUCTURE-BASED COMPUTATIONAL MICRO-MECHANICS OF HETEROGENEOUS MATERIALS.- A UNIFORM EXPRESSION OF INTERMOLECULAR POTENTIAL FUNCTIONS.- FULL-CHIP SIMULATION OF LSI LITHOGRAPHY MASK USING MULTI-SCALE ANALYSIS.- HIGH-ACCURACY AB INITIO PROGRAMS IN MOLECULAR SCIENCES WITH REFERENCE TO MOLPRO 2000.- MOLECULAR DYNAMICS SIMULATIONS OF NANOINDENTATION OF POSS MATERIALS.- STRESS CONCENTRATION FOR NANOCAVITIES FROM MOLECULAR SIMULATION.- ATOMISTIC SIMULATION ON THE STIFFENING AND SOFTENING MECHANISM OF NANOWIRES.- THE HIGH STRAIN-RATE SCALE FOR NANOWIRES.- VOXEL BASED RIGID BODY DYNAMICS FOR COMPUTER GRAPHICS.- COMPLEXITY OF CABLE DYNAMICS.- NUMERICAL SOLUTION OF ROBOT ARM MODEL USING STWS AND RKHEM TECHNIQUES.- ROTATION OF GRANULAR MATERIAL IN LABORATORY TESTS AND ITS NUMERICAL SIMULATION USING TIJ-COSSERAT CONTINUUM THEORY.- COMPARISON OF CLASSICAL MODELS FOR VISCOELASTICALLY DAMPED SANDWICH BEAMS.- MODELLING AND ANALYSES OF CRACKS IN FUSELAGE LAP JOINTS WITH A SINGLE-COUNTERSUNK RIVET.- THREE-DIMENSIONAL SOLUTION OF A DEEP BEAM USING AN EFFICIENT FINITE-DIFFERENCE SCHEME.- COMPUTATIONAL METHOD OF SEA LOADS ON FLOATING STRUCTURES.- NEW FORMULAS FOR DESIGN OF SOCKETS USED IN CABLE STRUCTURES.- A NOVEL SUBCYCLING ALGORITHM FOR COMPUTER SIMULATION OF CRASHWORTHINESS.- NUMERICAL ALGORITHM FOR DETERMINING HOPF BIFURCATION POINT OF NONLINEAR SYSTEM.- STUDY ON NONLINEAR DYNAMIC BEHAVIOURS AND STABILITY OF A FLEXIBLE ROTOR SYSTEM WITH HYDRODYNAMIC SLIDING BEARING SUPPORTS.- ON THE IMPERFECTIONS OF CYLINDRICAL SHELLS ON LOCAL SUPPORTS.- STUDY ON DOUBLY PERIODIC RIGID LINE INCLUSIONS UNDER ANTIPLANE SHEAR.- PRELIMINARY ANALYSIS OF NORMAL STRENGTH CONCRETE WALLS WITH OPENINGS USING LAYERED FINITE ELEMENT METHOD.- flexural behaviour of concrete beams reinforced with internal tensile steel and external cfrp.- 3D MODELLING OF BRITTLE FRACTURE IN HETEROGENEOUS ROCKS.- STRENGTH DETERIORATION OF NONPRISMATIC REINFORCED CONCRETE BEAMS.- NUMERICAL STUDY ON CONFINING PRESSURE EFFECT IN THE PROCESS OF ROCK FAILURE.- APPLICATION OF GEOSTATISTICAL WEIGHTS IN SOLVING PROBLEMS GOVERNED BY 2-D POISSON'S EQUATION USING FINITE POINT METHOD.- NUMERICAL APPROACH TO FRACTURES SATURATION BEHAVIOUR IN HETEROGENEOUS MATERIAL SUBJECTED TO THERMAL LOADING.- MICROMECHANICAL MODEL FOR SIMULATING THE HYDRAULIC FRACTURES OF ROCK.- A NEW NUMERICAL APPROACH FOR STUDYING SELF-ORGANIZED CRITICALITY BEHAVIOUR IN ROCK FAILURE PROCESS.- NUMERICAL APPROACH TO MINING INDUCED INSTANTANEOUS OUTBURSTS.- 3D NUMERICAL SIMULATION OF A LARGE SPAN DOUBLE-ARCH TUNNEL CONSTRUCTION.- AMPLITUDE FREQUENCY-LOAD CHARACTERISTIC RELATION OF CIRCULAR SANDWICH PLATES.- ENERGY ABSORPTION CAPACITY OF LAYERED FOAM CLADDING.- MODELLING BALLISTIC IMPACT ON WOVEN FABRIC WITH LS-DYNA.- RESEARCH FOR EXPLOSION OF HIGH EXPLOSIVE IN COMPLEX MEDIA.- NUMERICAL CALCULATION OF DETONATION PHENOMENON FOR EMULSION EXPLOSIVES.- NUMERICAL SIMULATION OF EXPLOSIVE FORMING.- FLEXIBLE AND INCOMPRESSIVE GOAL NETS IN SOCCER.- AERODYNAMIC PROPERTIES OF SOCCER BALL.- THE LEAKAGE ANALYSIS IN A TWIN-SCREW SUPERCHARGER BY USING AN INTEGRATED CAD\/CFD THREE-DIMENSIONAL MODEL.- A MULTI-AGENT TRAFFIC AND ENVIRONMENTAL SIMULATOR AND ITS APPLICATION TO THE ANALYSIS OF TRAFFIC CONGESTION IN KASHIWA CITY.- NUMERICAL INVESTIGATION OF COUPLED TRANSPORT OF IONS AND IONIC SOLUTION IN CORNEA AND THEIR INFLUENCE ON CORNEAL HYDRATION.- A QUASI-BUBBLE FINITE ELEMENT FORMULATION FOR THE SHALLOW WATER EQUATIONS WITH A DISCONTINUOUS BOUNDARY IMPLEMENTATION.- FREE VIBRATION ANALYSIS OF MULTIPLY CONNECTED PLATES USING THE METHOD OF FUNDAMENTAL SOLUTIONS.- a two-grid finite element discretization scheme for nonlinear eigenvalue problems.- IMPLEMENTING MINRES AND SYMMLQ FOR EIGENVALUE PROBLEMS.- VIBRATION OF A BEAM WITH A BREATHING CRACK SUBJECT TO MOVING MASS.- VALVE GEAR VIBRATIONAL ANALYSIS AND DEVELOPMENT OF NEW CAM DESIGN.- NATURAL FREQUENCY OF STEPPED BEAM HAVING MULTIPLE OPEN CRACKS BY TRANSFER MATRIX METHOD.- Fundamental Matrix Estimation Based On A Generalized Eigenvalue Problem.- SKELETAL REDUCTION OF EIGEN-VALUE PROBLEMS OVER THIN SOLIDS.- EXACT SOLUTION FOR THE FREE VIBRATION OF A TAPERED BEAM WITH ELASTIC END ROTATIONAL RESTRAINTS.","merchants_number":1,"ean":9781402039522,"category_id":1,"size":null,"min_price":299,"low_price_merchant_id":70255345,"ID":19768844,"merchants":["euniverse"],"brand":"Springer Netherlands,Springer","slug":"computational-methods-w-cd-rom-pt1","url":"\/produkt\/computational-methods-w-cd-rom-pt1\/","low_price_merchant_name":"eUniverse"}
Springer Netherlands,Springer
Computational Methods, w. CD-ROM. Pt.1
CHF 299.00
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Pt.1","deeplink":"https:\/\/cct.connects.ch\/tc.php?t=116298C1969900829T&subid=9781402039522&deepurl=https%3A%2F%2Feuniverse.ch%2Fbuecher%2Fmathematik-naturwissenschaft-technik%2Ftechnik%2F419296%2Fcomputational-methods-w.-cd-rom.-pt.1%3FsPartner%3Dtoppreise","labels":[],"brand_id":436598,"post_content":"TOPOLOGICAL OPTIMIZATION OF CONTINUUM STRUCTURE WITH GLOBAL STRESS CONSTRAINTS BASED ON ICM METHOD.- TOPOLOGICAL OPTIMIZATION OF FRAME STRUCTURES UNDER MULTIPLE LOADING CASES$^*$.- OPTIMAL DISPLACEMENT CONTROL SIMULATION OF ELECTRIC-MECHANICAL COUPLED TRUSSES.- PROTEIN SECONDARY STRUCTURE PREDICTION METHODS BASED ONRBF NEURAL NETWORKS.- A HYBRID META-HEURISTIC FOR A ROUTING PROBLEM.- IDENTIFICATION OF GEOMETRIC PARAMETERS OF DRAWBEAD USING NEURAL NETWORKS.- IMPROVED IMMUNE GENETIC ALGORITHM FOR SOLVING FLOW SHOP SCHEDULING PROBLEM.- A DISCRETE PARTICLE SWARM OPTIMIZATION ALGORITHM FOR TRAVELLING SALESMAN PROBLEM.- A SEARCH METHOD FOR FINDING A SIMPLE NASH EQUILIBRIUM.- A HERO EVOLUTIONARY ALGORITHM HYBRIDIZING FROM PSO AND GA.- A DATA COLLECTION MODEL FOR INTRUSION DETECTION SYSTEM BASED ON SIMPLE RANDOM SAMPLING.- DESTRUCTIVE EXTENSION RULE IN PROPOSITION MODAL LOGIC K.- A NOVEL PARTICLE SWARM OPTIMIZATION-BASED APPROACH FOR JOB-SHOP SCHEDULING.- UNCALIBRATED ROBOTIC ARM VISUAL SERVO CONTROL.- INTERLINEATION AND INTERFLATION FUNCTIONS OF MANY VARIABLES (BLENDING FUNCTION INTERPOLATION) AND ECONOMICAL ALGORITHMS IN THE APPROXIMATION THEORY.- INDEPENDENT COMPONENT ANALYSIS OF DYNAMIC CONTRAST-ENHANCED IMAGES: THE NUMBER OF COMPONENTS.- THE GEOMETRIC CONSTRAINT SOLVING BASED ON HYBRID GENETIC ALGORITHM OF CONJUGATE GRADIENT.- FAST IMAGE MOSAICS ALGORITHM USING PARTICLE SWARM OPTIMIZATION.- CHECKING CONSISTENCY IN HYBRID QUALITATIVE SPATIAL REASONING.- ICA-SCS DENOISING METHOD FOR WATERMARKING SCHEME.- STUDY AND IMPLEMENTATION OF SIMPLIFIED WWW DATA MODEL IN USE FOR WEB MINING.- AN EXTENSION OF EARLEY'S ALGORITHM FOR EXTENDED GRAMMARS.- THE RESEARCH ON POLICY-BASED REGISTRATION MECHANISM OF MOBILE TERMINALS IN MOBILE IP NETWORK.- OPERATIONAL SEMANTIC TO THE EXECUTION OF THE PROCESS MODEL.- FUZZY CLUSTERING ON THE WEB IMPLEMENTED BY JSP TECHNOLOGY.- TEXT CLASSIFICATION FOR CHINESE WEB DOCUMENTS.- LOGIC-BASED CONSTRAINT HANDLING IN RESOURCE-CONSTRAINED SCHEDULING PROBLEMS.- PARTICLE SWARM OPTIMIZATION METHOD USED IN PIXEL-BASED TEXTURE SYNTHESIS.- FORMAL METHOD IN IMPLEMENTATION OF ATLAS LANGUAGE*.- GENETIC ALGORITHM FOR EVALUATION METRICS IN TOPICAL WEB CRAWLING.- A BOUNDARY METHOD TO SPEED UP TRAINING SUPPORT VECTOR MACHINES.- A FAST ALGORITHM FOR GENERATING CONCEPTS USING AN ATTRIBUTE TABLE.- ALGORITHM OF MEASUREMENT-BASED ADMISSION CONTROL FOR GPRS.- SURFACE RECOGNITION OF AUTOMOBILE PANEL BASED ON SECTION CURVE IDENTIFICATION.- DYNAMIC CLUSTERING ALGORITHM BASED ON ADAPTIVE RESONANCE THEORY.- ONTOLOGY LEARNING USING WORDNET LEXICON.- GENETIC PROGRAMMING FOR MAXIMUM-LIKELIHOOD PHYLOGENY INFERENCE.- MINING DOMINANCE ASSOCIATION RULES IN PREFERENCE-ORDERED DATA.- Mining Ordinal Patterns For Data Cleaning.- USER ASSOCIATION MINING BASED ON CONCEPT LATTICE.- A PLM-ORIENTED WORKFLOW MODEL.- A LANGUAGE FOR SPECIFYING CONSTRAINTS IN WFMSs.- ONTOLOGY BASED WORKFLOW MODEL.- CONSTRAINED MULTI-SAMPLE TEXTURE SYNTHESIS.- A GENERAL INCREMENTAL HIERARCHICAL CLUSTERING METHOD.- ACTIVATING IRREGULAR DIMENSIONS IN OLAP.- A NEW COMPUTATIONAL METHOD OF INTERSECTION FOR RAY TRACING.- STUDY ON PARTNERS SELECTION OF AGILE VIRTUAL ENTERPRISE BASED ON PARTICLE SWARM OPTIMIZATION.- ADAPTIVE DIRECTIONAL WEIGHTED MEDIAN FILTERING.- AN INTERACTIVE WORKFLOW MANAGEMENT FUNCTION MODEL.- PACKET FAIR SCHEDULING ALGORITHM BASED ON WEIGHTS DYNAMIC COMPENSATION.- EXTENDED ADAPTIVE WEIGHTED AVERAGING FILTER MODEL.- AN IMPROVED QUANTUM-INSPIRED EVOLUTIONARY ALGORITHM FOR CLUSTERING GENE EXPRESSION DATA.- AUTOMATIC BUFFER OVERFLOW DETECTION BASED ON OPERATION SEMANTIC.- QUANTUM-INSPIRED EVOLUTIONARY ALGORITHM FOR TRAVELLING SALESMAN PROBLEM.- AN IMPROVED COEVOLUTION ALGORITHM FOR FUZZY MODELLING.- A COMPUTATIONAL METHOD FOR SOLVING CAUCHY PROBLEMS OF ELLIPTIC OPERATORS.- NUMERICAL DETERMINATION OF THE RESONANCE FREQUENCIES AND EIGENMODES USING THE MFS.- SCATTERED NODE COMPACT FINITE DIFFERENCE-TYPE FORMULAS GENERATED FROM RADIAL BASIS FUNCTIONS.- EXPLOSION SIMULATION BY SMOOTHED PARTICLE HYDRODYNAMICS.- CLASSIC TAYLOR-BAR IMPACT TEST REVISITED USING 3D SPH.- A MESH FREE METHOD BASED ON AN OPTIMIZATION TECHNIQUE AND THE MOVING LEAST SQUARES APPROXIMATION.- FREE VIBRATION ANALYSIS OF TIMOSHENKO BEAMS BY RADIAL BASIS FUNCTIONS.- MOVING LEAST SQUARE SPH USING FIXED KERNEL FOR LARGE DEFORMATION ELASTO-PLASTIC ANALYSIS.- A COUPLED MESHFREE\/SCALED BOUNDARY METHOD.- BASIC DISCUSSION OF BOUNDARY CONDITION OF SMOOTHED PARTICLE HYDRODYNAMICS FOR ANALYSIS OF CEREBRAL CONTUSION.- DEPOSITION OF COLLOIDAL PARTICLES FROM PRESSURE DRIVEN MICROFLUIDIC FLOW-BROWNIAN DYNAMICS SIMULATION.- S SHAPE PARAMETERS OF MULTIQUADRICS IN THE HEAVISIDE WEIGHTED MLPG METHOD.- APPLICATION OF HIGH ORDER BASIS FUNCTIONS IN SOLID MECHANICS BY ELEMENT FREE GALERKIN (EFG) METHOD.- AN ADAPTIVE MESHFREE COLLOCATION METHOD FOR STATIC AND DYNAMIC NONLINEAR PROBLEMS.- MESHLESS NATURAL NEIGHBOUR METHOD AND ITS APPLICATION IN ELASTO-PLASTIC PROBLEMS.- A MESHLESS LOCAL PETROV-GALERKIN METHOD FOR ELASTO-PLASTIC PROBLEMS.- PARTICLE-PARTITION OF UNITY METHODS IN ELASTICITY.- A MESHFREE APPROXIMATION WITH ALLMAN'S ROTATIONAL DOFS.- THE VORTEX METHOD APPLIED TO SIMULATION OF HOMOGENEOUS ISOTROPIC TURBULENCE.- AN APPLICATION OF THE LOCAL PETROV-GALERKIN METHOD IN SOLVING GEOMETRICALLY NONLINEAR PROBLEMS.- ELASTIC-PLASTIC LARGE DEFORMATION ANALYSIS USING SPH.- TWO ALGORITHMS FOR SUPERCONVERGENT STRESS RECOVERY BASED ON MLS AND FINITE POINTS METHOD.- GEOMETRICALLY NONLINEAR ANALYSIS USING MESHFREE RPIM.- GALERKIN MESHLESS METHODS BASED ON PARTITION OF UNITY QUADRATURE.- RADIAL POINT INTERPOLATION COLLOCATION METHOD (RPICM) USING UPWIND BIASED LOCAL SUPPORT SCHEME FOR SOLVING CONVECTION-DOMINATED EQUATIONS.- MESHFREE NUMERICAL SOLUTION OF TWO-PHASE FLOW THROUGH POROUS MEDIA.- STRESS ANALYSIS OF 3-D SOLIDS USING A MESHFREE RADIAL POINT INTERPOLATION METHOD.- 3-D HEAT TRANSFER ANALYSIS USING A COLLOCATION METHOD TOGETHER WITH RPIM SHAPE FUNCTIONS AND FIC BOUNDARY CONDITIONS.- SYMPLECTIC ANALYSIS FOR OPTICAL WAVEGUIDES IN LAYERED MEDIA.- A COMPUTATIONAL ANALYSIS OF THERMAL RESIDUAL STRESS DURING MAGNETIC QUENCHING.- PARALLEL FEM ANALYSIS OF HIGH FREQUENCY ELECTROMAGNETIC WAVE IN AN ENVIRONMENT.- AND Ta ATOMS IN SILK-LIKE AMORPHOUS POLYMER.- MULTISCALE COUPLING OF MESHLESS METHOD AND MOLECULAR DYNAMICS.- MECHANISMS OF DISINTEGRATION OF MINERAL MEDIA EXPOSED TO HIGH-POWER ELECTROMAGNETIC PULSES.- DOMAIN SWITCHING CRITERIA FOR TETRAGONAL PHASE FERROELECTRICS: A COMPARATIVE STUDY.- GENERALIZED MAGNETO-THERMOELASTICITY SOLVED BY FEM IN TIME DOMAIN.- MICROSTRUCTURE REPRESENTATION AND SIMULATION TOOLS FOR MICROSTRUCTURE-BASED COMPUTATIONAL MICRO-MECHANICS OF HETEROGENEOUS MATERIALS.- A UNIFORM EXPRESSION OF INTERMOLECULAR POTENTIAL FUNCTIONS.- FULL-CHIP SIMULATION OF LSI LITHOGRAPHY MASK USING MULTI-SCALE ANALYSIS.- HIGH-ACCURACY AB INITIO PROGRAMS IN MOLECULAR SCIENCES WITH REFERENCE TO MOLPRO 2000.- MOLECULAR DYNAMICS SIMULATIONS OF NANOINDENTATION OF POSS MATERIALS.- STRESS CONCENTRATION FOR NANOCAVITIES FROM MOLECULAR SIMULATION.- ATOMISTIC SIMULATION ON THE STIFFENING AND SOFTENING MECHANISM OF NANOWIRES.- THE HIGH STRAIN-RATE SCALE FOR NANOWIRES.- VOXEL BASED RIGID BODY DYNAMICS FOR COMPUTER GRAPHICS.- COMPLEXITY OF CABLE DYNAMICS.- NUMERICAL SOLUTION OF ROBOT ARM MODEL USING STWS AND RKHEM TECHNIQUES.- ROTATION OF GRANULAR MATERIAL IN LABORATORY TESTS AND ITS NUMERICAL SIMULATION USING TIJ-COSSERAT CONTINUUM THEORY.- COMPARISON OF CLASSICAL MODELS FOR VISCOELASTICALLY DAMPED SANDWICH BEAMS.- MODELLING AND ANALYSES OF CRACKS IN FUSELAGE LAP JOINTS WITH A SINGLE-COUNTERSUNK RIVET.- THREE-DIMENSIONAL SOLUTION OF A DEEP BEAM USING AN EFFICIENT FINITE-DIFFERENCE SCHEME.- COMPUTATIONAL METHOD OF SEA LOADS ON FLOATING STRUCTURES.- NEW FORMULAS FOR DESIGN OF SOCKETS USED IN CABLE STRUCTURES.- A NOVEL SUBCYCLING ALGORITHM FOR COMPUTER SIMULATION OF CRASHWORTHINESS.- NUMERICAL ALGORITHM FOR DETERMINING HOPF BIFURCATION POINT OF NONLINEAR SYSTEM.- STUDY ON NONLINEAR DYNAMIC BEHAVIOURS AND STABILITY OF A FLEXIBLE ROTOR SYSTEM WITH HYDRODYNAMIC SLIDING BEARING SUPPORTS.- ON THE IMPERFECTIONS OF CYLINDRICAL SHELLS ON LOCAL SUPPORTS.- STUDY ON DOUBLY PERIODIC RIGID LINE INCLUSIONS UNDER ANTIPLANE SHEAR.- PRELIMINARY ANALYSIS OF NORMAL STRENGTH CONCRETE WALLS WITH OPENINGS USING LAYERED FINITE ELEMENT METHOD.- flexural behaviour of concrete beams reinforced with internal tensile steel and external cfrp.- 3D MODELLING OF BRITTLE FRACTURE IN HETEROGENEOUS ROCKS.- STRENGTH DETERIORATION OF NONPRISMATIC REINFORCED CONCRETE BEAMS.- NUMERICAL STUDY ON CONFINING PRESSURE EFFECT IN THE PROCESS OF ROCK FAILURE.- APPLICATION OF GEOSTATISTICAL WEIGHTS IN SOLVING PROBLEMS GOVERNED BY 2-D POISSON'S EQUATION USING FINITE POINT METHOD.- NUMERICAL APPROACH TO FRACTURES SATURATION BEHAVIOUR IN HETEROGENEOUS MATERIAL SUBJECTED TO THERMAL LOADING.- MICROMECHANICAL MODEL FOR SIMULATING THE HYDRAULIC FRACTURES OF ROCK.- A NEW NUMERICAL APPROACH FOR STUDYING SELF-ORGANIZED CRITICALITY BEHAVIOUR IN ROCK FAILURE PROCESS.- NUMERICAL APPROACH TO MINING INDUCED INSTANTANEOUS OUTBURSTS.- 3D NUMERICAL SIMULATION OF A LARGE SPAN DOUBLE-ARCH TUNNEL CONSTRUCTION.- AMPLITUDE FREQUENCY-LOAD CHARACTERISTIC RELATION OF CIRCULAR SANDWICH PLATES.- ENERGY ABSORPTION CAPACITY OF LAYERED FOAM CLADDING.- MODELLING BALLISTIC IMPACT ON WOVEN FABRIC WITH LS-DYNA.- RESEARCH FOR EXPLOSION OF HIGH EXPLOSIVE IN COMPLEX MEDIA.- NUMERICAL CALCULATION OF DETONATION PHENOMENON FOR EMULSION EXPLOSIVES.- NUMERICAL SIMULATION OF EXPLOSIVE FORMING.- FLEXIBLE AND INCOMPRESSIVE GOAL NETS IN SOCCER.- AERODYNAMIC PROPERTIES OF SOCCER BALL.- THE LEAKAGE ANALYSIS IN A TWIN-SCREW SUPERCHARGER BY USING AN INTEGRATED CAD\/CFD THREE-DIMENSIONAL MODEL.- A MULTI-AGENT TRAFFIC AND ENVIRONMENTAL SIMULATOR AND ITS APPLICATION TO THE ANALYSIS OF TRAFFIC CONGESTION IN KASHIWA CITY.- NUMERICAL INVESTIGATION OF COUPLED TRANSPORT OF IONS AND IONIC SOLUTION IN CORNEA AND THEIR INFLUENCE ON CORNEAL HYDRATION.- A QUASI-BUBBLE FINITE ELEMENT FORMULATION FOR THE SHALLOW WATER EQUATIONS WITH A DISCONTINUOUS BOUNDARY IMPLEMENTATION.- FREE VIBRATION ANALYSIS OF MULTIPLY CONNECTED PLATES USING THE METHOD OF FUNDAMENTAL SOLUTIONS.- a two-grid finite element discretization scheme for nonlinear eigenvalue problems.- IMPLEMENTING MINRES AND SYMMLQ FOR EIGENVALUE PROBLEMS.- VIBRATION OF A BEAM WITH A BREATHING CRACK SUBJECT TO MOVING MASS.- VALVE GEAR VIBRATIONAL ANALYSIS AND DEVELOPMENT OF NEW CAM DESIGN.- NATURAL FREQUENCY OF STEPPED BEAM HAVING MULTIPLE OPEN CRACKS BY TRANSFER MATRIX METHOD.- Fundamental Matrix Estimation Based On A Generalized Eigenvalue Problem.- SKELETAL REDUCTION OF EIGEN-VALUE PROBLEMS OVER THIN SOLIDS.- EXACT SOLUTION FOR THE FREE VIBRATION OF A TAPERED BEAM WITH ELASTIC END ROTATIONAL RESTRAINTS.","merchants_number":1,"ean":9781402039522,"category_id":1,"size":null,"min_price":299,"low_price_merchant_id":70255345,"ID":19768844,"merchants":["euniverse"],"brand":"Springer Netherlands,Springer","slug":"computational-methods-w-cd-rom-pt1","url":"\/produkt\/computational-methods-w-cd-rom-pt1\/","low_price_merchant_name":"eUniverse"}