Bayesian cognitive modeling : a practical course / Michael D. Lee and Eric-Jan Wagenmakers.

Includes bibliographical references and index.

Part I. Getting Started:
1. The basics of Bayesian analysis;
2. Getting started with WinBUGS;
Part II. Parameter Estimation:
3. Inferences with binomials;
4. Inferences with Gaussians;
5. Some examples of data analysis;
6. Latent mixture models;
Part III. Model Selection:
7. Bayesian model comparison;
8. Comparing Gaussian means;
9. Comparing binomial rates;
Part IV. Case Studies:
10. Memory retention;
11. Signal detection theory;
12. Psychophysical functions;
13. Extrasensory perception;
14. Multinomial processing trees;
15. The SIMPLE model of memory;
16. The BART model of risk taking;
17. The GCM model of categorization;
18. Heuristic decision-making;
19. Number concept development.

This book demonstrates how to do Bayesian modeling. Short, to-the-point chapters offer examples, exercises, and computer code (using WinBUGS or JAGS, and supported by Matlab and R), with additional support available online. No advance knowledge of statistics is required and, from the very start, readers are encouraged to apply and adjust Bayesian analyses by themselves. The book contains a series of chapters on parameter estimation and model selection, followed by detailed case studies from cognitive science. After working through this book, readers should be able to build their own Bayesian models, apply the models to their own data, and draw their own conclusions.