Doing Bayesian Data Analysis A Tutorial Introduction with R

Name: Doing Bayesian Data Analysis A Tutorial Introduction with R
Price: 5.47 USD
Availability: InStock
ISBN: 9780123814852

ISBN-10: 0123814855

ISBN-13: 9780123814852

Edition: 2011

Authors: John Kruschke

List price: $61.99

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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 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…

Book details

List price: $61.99
Copyright year: 2011
Publisher: Elsevier Science & Technology
Publication date: 11/25/2010
Binding: Hardcover
Pages: 672
Size: 7.50" wide x 9.21" long x 1.25" tall
Weight: 2.750
Language: English



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