Doing Bayesian Data Analysis A Tutorial with R and Bugs

ISBN-10: 0123814855
ISBN-13: 9780123814852
Edition: 2011
Authors: John K. Kruschke
List price: $54.99 Buy it from $33.60
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Description: There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and  More...

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Book details

List price: $54.99
Copyright year: 2011
Publisher: Elsevier Science & Technology
Publication date: 11/25/2010
Binding: Hardcover
Pages: 672
Size: 7.75" wide x 9.75" long x 1.25" tall
Weight: 2.750
Language: English

There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, provides an accessible approach to Bayesian Data Analysis, as material is explained clearly with concrete examples. The book begins with the basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data. The text delivers comprehensive coverage of all scenarios addressed by non-Bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, correlation, multiple regression, and chi-square (contingency table analysis). This book is intended for first year graduate students or advanced undergraduates. It provides a bridge between undergraduate training and modern Bayesian methods for data analysis, which is becoming the accepted research standard. Prerequisite is knowledge of algebra and basic calculus.Accessible, including the basics of essential concepts of probability and random sampling(UNIQUE) Examples with R programming language and BUGS softwareComprehensive coverage of all scenarios addressed by non-bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, correlation, multiple regression, and chi-square (contingency table analysis).Coverage of experiment planningR and BUGS computer programming code on websitevideo's that correspond to various parts of the bookgraded difficulty level of exercises- easier to hardest

This Book's Organization: Read me First!
The Basics: Parameters, Probability, Bayes' Rule and R
What is this stuff called probability?
Bayes' Rule
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
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
Tools in the Trunk
Reparameterization, a.k.a. Change of Variables
References
Index

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