"An introduction to constructing and reasoning with Bayesian (probabilistic) models of perceptual decision making and action, modeling how the brain perceives and decides"--
"An introduction to constructing and reasoning with Bayesian (probabilistic) models of perceptual decision making and action, modeling how the brain perceives and decides"--Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Wei Ji Ma, Konrad Paul Kording, and Daniel Goldreich
Inhaltsangabe
Acknowledgments xv The Four Steps of Bayesian Modeling xvii List of Acronyms xix Introduction 1 1 Uncertainty and Inference 7 2 Using Bayes' Rule 31 3 Bayesian Inference under Measurement Noise 53 4 The Response Distribution 83 5 Cue Combination and Evidence Accumulation 105 6 Learning as Inference 125 7 Discrimination and Detection 147 8 Binary Classification 169 9 Top-Level Nuisance Variables and Ambiguity 191 10 Same-Different Judgment 205 11 Search 227 12 Inference in a Changing World 245 13 Combining Inference with Utility 257 14 The Neural Likelihood Function 281 15 Bayesian Models in Context 301 Appendices 311 A Notation 313 B Basics of Probability Theory 315 C Model Fitting and Model Comparison 343 Bibliography 361 Index 371
Acknowledgments xv The Four Steps of Bayesian Modeling xvii List of Acronyms xix Introduction 1 1 Uncertainty and Inference 7 2 Using Bayes' Rule 31 3 Bayesian Inference under Measurement Noise 53 4 The Response Distribution 83 5 Cue Combination and Evidence Accumulation 105 6 Learning as Inference 125 7 Discrimination and Detection 147 8 Binary Classification 169 9 Top-Level Nuisance Variables and Ambiguity 191 10 Same-Different Judgment 205 11 Search 227 12 Inference in a Changing World 245 13 Combining Inference with Utility 257 14 The Neural Likelihood Function 281 15 Bayesian Models in Context 301 Appendices 311 A Notation 313 B Basics of Probability Theory 315 C Model Fitting and Model Comparison 343 Bibliography 361 Index 371
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