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

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

Hardback Academic Press

By (author) Kruschke John

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  • Publisher: Academic Press Inc
  • Format: Hardback | 672 pages
  • Dimensions: 195mm x 250mm x 20mm | 699g
  • Publication date: 1 December 2010
  • Publication City/Country: San Diego
  • ISBN 10: 0123814855
  • ISBN 13: 9780123814852
  • Illustrations note: Approx. 175 illustrations
  • Sales rank: 49,264

Product description

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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Review quote

"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

Table of contents

This Book's Organization: Read me First!; The Basics: Parameters, Probability, Bayes' Rule and R; What is this stuff called probability?; Bayes' Rule; Part II All the Fundamental Concepts and Techniques in a Simple Scenario; Inferring a Binomial Proportion via Exact mathematical Analysis; Inferring a Binomial Proportion via Grid Approximation; Inferring a Binomial Proportion via Monte Carlo Methods; Inferences Regarding Two Binomial Proportions; Bernoulli Likelihood with Hierarchical Prior; Hierarchical modeling and model comparison; Null Hypothesis Significance Testing; Bayesian Approaches to Testing a Point ("Null") Hypothesis; Goals, Power, and Sample Size; Part III The Generalized Linear Model; Overview of the Generalized Linear Model; Metric Predicted Variable on a Single Group; Metric Predicted Variable with One Metric Predictor; Metric Predicted Variable with Multiple Metric Predictors; Metric Predicted Variable with One Nominal Predictor; Metric Predicted Variable with Multiple Nominal Predictors; Dichotomous Predicted Variable; Original Predicted Variable, Contingency Table Analysis; Part IV Tools in the Trunk; Reparameterization, a.k.a. Change of Variables; References; Index