Preface
Part I: The Basics
1 Introducing the Bayesian approach to cognitive science
2 Probabilistic models of cognition in historical context
3 Bayesian inference
4 Graphical models
5 Building complex generative models
6 Approximate probabilistic inference
7 From probabilities to actions
Part II: Advanced Topics
8 Learning inductive bias with hierarchical Bayesian models
9 Capturing the growth of knowledge with nonparametric Bayesian models
10 Estimating subjective probability distributions
11 Sampling as a bridge across levels of analysis
12 Bayesian models and neural networks
13 Resource-rational analysis
14 Theory of mind and inverse planning
15 Intuitive physics as probabilistic inference
16 Language processing and language learning
17 Bayesian inference over logical representations
18 Probabilistic programs as a unifying language of thought
19 Learning as Bayesian inference over programs
20 Bayesian models of cognitive development
21 The limits of inference and algorithmic probability
22 A Bayesian conversation
Conclusion
Acknowledgments
References
