Doing Bayesian Data Analysis

Doing Bayesian Data Analysis : A Tutorial Introduction with R and BUGS

By (author) Kruschke John

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There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, is for first year graduate students or advanced undergraduates and provides an accessible approach, as all mathematics is explained intuitively and with concrete examples. It assumes only algebra and 'rusty' calculus. Unlike other textbooks, this book begins with the basics, including essential concepts of probability and random sampling. This book gradually climbs all the way to advanced hierarchical modeling methods for realistic data. The text provides complete examples with the R programming language and BUGS software (both freeware), and begins with basic programming examples, working up gradually to complete programs for complex analyses and presentation graphics. These templates can be easily adapted for a large variety of students and their own research needs. The textbook bridges the students from their undergraduate training into modern Bayesian methods. It provides complete examples with R programming language and BUGS software (both Freeware). It addresses topics such as experiment planning, power analysis and sample size planning. It includes numerous exercises with explicit purposes and guidelines for accomplishment.

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  • Hardback | 672 pages
  • 193.04 x 241.3 x 35.56mm | 1,247.37g
  • 01 Dec 2010
  • Elsevier Science Publishing Co Inc
  • Academic Press Inc
  • San Diego
  • English
  • Approx. 175 illustrations
  • 0123814855
  • 9780123814852
  • 78,748

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"This book is head-and-shoulders better than the others I've seen. I'm using it myself right now. Here's what's good about it: .It builds from very simple foundations. .Math is minimized. No proofs. .From start to finish, everything is demonstrated through R programs. .It helps you learn Empirical Bayesian methods from every angle."--Exploring Possibility Space blog, March 12, 2014

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