BOOK

Bayesian Cognitive Modeling: A Practical Course

Abstract

Bayesian inference has become a standard method of analysis in many fields of science. Students and researchers in experimental psychology and cognitive science, however, have failed to take full advantage of the new and exciting possibilities that the Bayesian approach affords. Ideal for teaching and self study, 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

Keywords:
Bayesian probability Multinomial distribution Computer science Artificial intelligence Mixture model Bayesian inference Model selection Machine learning Categorization Statistics Econometrics Mathematics

Metrics

1422
Cited By
20.52
FWCI (Field Weighted Citation Impact)
184
Refs
0.99
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Bayesian Modeling and Causal Inference
Physical Sciences →  Computer Science →  Artificial Intelligence
Cognitive Science and Education Research
Life Sciences →  Neuroscience →  Cognitive Neuroscience
Child and Animal Learning Development
Social Sciences →  Psychology →  Developmental and Educational Psychology

Related Documents

© 2026 ScienceGate Book Chapters — All rights reserved.