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Synopsis
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.
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Book Description
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.
About the Author
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.
"About this title" may belong to another edition of this title.
- PublisherCambridge University Press
- Publication date2014
- ISBN 10 1107018455
- ISBN 13 9781107018457
- BindingHardcover
- LanguageEnglish
- Number of pages280
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Hardcover. Condition: new. Hardcover. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781107018457
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Hardcover. Condition: new. Hardcover. 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. 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9781107018457
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