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Bayesian Cognitive Modeling

  • Livre Relié
  • 280 Nombre de pages
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Informationen zum Autor Michael D. Lee is a professor in the Department of Cognitive Sciences at the University of California, Irv... Lire la suite
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Description

Informationen zum Autor Michael D. Lee is a professor in the Department of Cognitive Sciences at the University of California, Irvine. Eric-Jan Wagenmakers is a professor in the Department of Psychological Methods at the University of Amsterdam. Klappentext Using a practical, hands-on approach, this book will teach anyone how to carry out Bayesian analyses and interpret the results. Zusammenfassung Ideal for teaching and self study, this practical book demonstrates how cognitive scientists can conduct Bayesian analyses for many real-life modeling problems. Supported by examples, exercises, computer code and additional resources available online, readers will learn to take full advantage of the exciting possibilities that the Bayesian approach affords. Inhaltsverzeichnis 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.

Texte du rabat

Using a practical, hands-on approach, this book will teach anyone how to carry out Bayesian analyses and interpret the results.



Résumé
Ideal for teaching and self study, this practical book demonstrates how cognitive scientists can conduct Bayesian analyses for many real-life modeling problems. Supported by examples, exercises, computer code and additional resources available online, readers will learn to take full advantage of the exciting possibilities that the Bayesian approach affords.

Contenu

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.

Informations sur le produit

Titre: Bayesian Cognitive Modeling
Sous-titre: A Practical Course
Auteur:
Code EAN: 9781107018457
ISBN: 978-1-107-01845-7
Format: Livre Relié
Editeur: Cambridge Univ Pr
Genre: Psychologie
nombre de pages: 280
Poids: 703g
Taille: H249mm x B188mm x T20mm
Année: 2014
Auflage: New

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