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    Doing Bayesian Data Analysis: A Tutorial Introduction with R and BUGS (Academic Press) (Hardback) By (author) Kruschke John

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    DescriptionThere 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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  • Full bibliographic data for Doing Bayesian Data Analysis

    Title
    Doing Bayesian Data Analysis
    Subtitle
    A Tutorial Introduction with R and BUGS
    Authors and contributors
    By (author) Kruschke John
    Physical properties
    Format: Hardback
    Number of pages: 672
    Width: 195 mm
    Height: 250 mm
    Thickness: 20 mm
    Weight: 699 g
    Language
    English
    ISBN
    ISBN 13: 9780123814852
    ISBN 10: 0123814855
    Classifications

    BIC E4L: MAT
    B&T Book Type: NF
    Nielsen BookScan Product Class 3: S7.9T
    B&T Merchandise Category: TXT
    B&T Modifier: Region of Publication: 01
    BIC subject category V2: PBW
    B&T General Subject: 710
    B&T Modifier: Academic Level: 02
    Ingram Subject Code: MA
    B&T Modifier: Text Format: 06, 01
    Warengruppen-Systematik des deutschen Buchhandels: 16280
    BISAC V2.8: MAT029000
    LC subject heading:
    BISAC V2.8: MAT000000
    LC subject heading:
    DC22: 519.542
    LC subject heading:
    DC22: 519.5/42
    LC classification: QA279.5 .K79 2011
    Thema V1.0: PBW
    Illustrations note
    Approx. 175 illustrations
    Publisher
    Elsevier Science Publishing Co Inc
    Imprint name
    Academic Press Inc
    Publication date
    01 December 2010
    Publication City/Country
    San Diego
    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