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
