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Bayesian Cognitive Modeling
A Practical Course

$137.00 (P)

  • Date Published: April 2014
  • availability: Available
  • format: Hardback
  • isbn: 9781107018457

$ 137.00 (P)

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About the Authors
  • 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.

    • Offers a practical, hands-on approach to teach readers how to carry out Bayesian analyses and interpret the results
    • No advance knowledge of statistics or mathematics is required
    • Interesting and relevant examples illustrate how Bayesian modeling can be useful for problems that people in cognitive science care about
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    Reviews & endorsements

    "This book provides the best practical guide to date on how to do Bayesian modeling in cognitive science."
    --Jay Myung, Professor of Psychology, Ohio State University

    "This is a very powerful exposition of how Bayesian methods, and WinBUGS in particular, can be used to deal with cognitive models that are apparently intractable. When we produced WinBUGS, we had no idea it could be used like this - it's amazing and gratifying to see these applications."
    --David Spiegelhalter, Winton Professor for the Public Understanding of Risk, Statistical Laboratory, Centre for Mathematical Sciences, Cambridge

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    Product details

    • Date Published: April 2014
    • format: Hardback
    • isbn: 9781107018457
    • length: 280 pages
    • dimensions: 254 x 178 x 17 mm
    • weight: 0.7kg
    • availability: Available
  • Table of Contents

    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.

  • Authors

    Michael D. Lee, University of California, Irvine
    Michael D. Lee is a professor in the Department of Cognitive Sciences at the University of California, Irvine.

    Eric-Jan Wagenmakers, Universiteit van Amsterdam
    Eric-Jan Wagenmakers is a professor in the Department of Psychological Methods at the University of Amsterdam.

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