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Bayesian Cognitive Modeling: A Practical Course [Kindle Edition]

Michael D. Lee , Eric-Jan Wagenmakers

Print List Price: £27.99
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Book Description

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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Review

'This book provides the best practical guide to date on how to do Bayesian modeling in cognitive science.' Jay Myung, 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

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.

Product details

  • Format: Kindle Edition
  • File Size: 6310 KB
  • Print Length: 280 pages
  • Simultaneous Device Usage: Up to 4 simultaneous devices, per publisher limits
  • Publisher: Cambridge University Press (31 Dec. 2013)
  • Sold by: Amazon Media EU S.à r.l.
  • Language: English
  • ASIN: B00GA22N02
  • Text-to-Speech: Enabled
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  • Enhanced Typesetting: Not Enabled
  • Amazon Bestsellers Rank: #475,411 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Customer Reviews

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Most Helpful Customer Reviews on Amazon.com (beta)
Amazon.com: 5.0 out of 5 stars  6 reviews
4 of 4 people found the following review helpful
5.0 out of 5 stars Bayesian statistics provide a much better way of fitting and testing models in many areas ... 10 July 2014
By Antonio Rangel - Published on Amazon.com
Format:Hardcover
Bayesian statistics provide a much better way of fitting and testing models in many areas of neuroscience, psychology, and economics. They are especially useful in settings in which there is a latent natural hierarchical structure and in which the models of interest are complex. If you are interested in learning how to use this methods in any of these areas, this book is as useful and as good as it gets. It will bring you up to speed on the key techniques and how to use them effectively. The fact that this version supports multiple statistical softwares, and not only R, is an added plus. Many people in my lab have used it successfully to learn hese methods.

Antonio Rangel
Caltech
www.rnl.caltech.edu
2 of 3 people found the following review helpful
5.0 out of 5 stars I think this well-written book is most useful for anyone who wants become acquainted with modern Bayesian ... 15 July 2014
By Prof. Dr. Rolf Ulrich - Published on Amazon.com
Format:Paperback
I think this well-written book is most useful for anyone who wants become acquainted with modern Bayesian interference and in particular with WinBUGS and JAGS. I found this book especially relevant for experimental psychologists and cognitive scientists. It covers many practical problems including parameter estimation, statistical interference, and basic cognitive models (e.g., signal detection theory, multinomial processing trees, decision making). For each problem, the authors provide a computer code, which readers with little background in Matlab or R will appreciate. After working out some of these examples, it is easy to see how the general principle of this interference can be applied to other statistical problems and cognitive models.
1 of 1 people found the following review helpful
5.0 out of 5 stars Worth reading 9 Feb. 2015
By David Lehmann - Published on Amazon.com
Format:Paperback|Verified Purchase
Good reference covers topic well. Should not be your first reference.
2 of 3 people found the following review helpful
5.0 out of 5 stars Great primer on Bayesian computational cogntion 11 July 2014
By Amazon Customer - Published on Amazon.com
Format:Paperback
This is a great primer on how to build and test Bayesian models of cognitive processes, written for students of cognitive science who are new to both computational modeling and Bayesian inference. What is particularly noteworthy of the book is its practical approach to learning, with plenty of hands-on and work-through examples and case studies with real data sets.
1 of 3 people found the following review helpful
5.0 out of 5 stars This is an excellent source to learn Bayes for researchers 20 Aug. 2014
By wolf vanpaemel - Published on Amazon.com
Format:Hardcover
This is an excellent source to learn Bayes for researchers, instructors and students, neatly demonstrating the almost overwhelming flexibility the Bayesian approach has to offer using hands on, worked examples from real research.
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