Applied Bayesian statistics

Applied Bayesian statistics: with R and OpenBUGS examples

by Cowles, Mary Kathryn

Type Book
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This book is based on over a dozen years teaching a Bayesian Statistics course. The material presented here has been used by students of different levels and disciplines, including advanced undergraduates studying Mathematics and Statistics and students in graduate programs in Statistics, Biostatistics, Engineering, Economics, Marketing, Pharmacy, and Psychology. The goal of the book is to impart the basics of designing and carrying out Bayesian analyses, and interpreting and communicating the …

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Title Statements

  • Full title :Applied Bayesian statistics : with R and OpenBUGS examples / Mary Kathryn Cowles.

Summary

This book is based on over a dozen years teaching a Bayesian Statistics course. The material presented here has been used by students of different levels and disciplines, including advanced undergraduates studying Mathematics and Statistics and students in graduate programs in Statistics, Biostatistics, Engineering, Economics, Marketing, Pharmacy, and Psychology. The goal of the book is to impart the basics of designing and carrying out Bayesian analyses, and interpreting and communicating the results. In addition, readers will learn to use the predominant software for Bayesian model-fitting.

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Edition Statements

  • Publication Date:©2013.

Publication Statements

  • Publisher(s):Springer
  • Place of Publication:New York
  • Language:eng
  • Publication Date:©2013.

Notes

Titre de l'écran-titre (visionné le 12 mars 2013).

The Physical Object

  • Description:1 online resource (1 texte électronique (xiv, 232 p.))

Edition Identifiers

  • ISBN:1461456967, 9781461456964
  • oclc:ocn832593337
  • LC Classification:QA279.5
  • Dewey Decimal:300.1/519542

Table of Contents

What is Bayesian statistics? -- Review of probability -- Introduction to one-parameter models -- Inference for a population proportion -- Special considerations in Bayesian inference -- Other one-parameter models and their conjugate priors -- More realism please: Introduction to multiparameter models -- Fitting more complex Bayesian models: Markov chain Monte Carlo -- Hierarchical models, and more on convergence assessment -- Regression and hierarchical regression models -- Model Comparison, Model Checking, and Hypothesis Testing -- References -- Index.

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Subjects

Bayesian statistical decision theory e-books Livres numériques Théorie de la décision bayésienne

Writers

Cowles, Mary Kathryn

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