Applied Bayesian statistics : with R and OpenBUGS examples

Author(s)

    • Cowles, Mary Kate

Bibliographic Information

Applied Bayesian statistics : with R and OpenBUGS examples

Mary Kathryn Cowles

(Springer texts in statistics)

Springer, c2013

Available at  / 12 libraries

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Description and Table of Contents

Description

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, R and OpenBUGS. The practical approach this book takes will help students of all levels to build understanding of the concepts and procedures required to answer real questions by performing Bayesian analysis of real data. Topics covered include comparing and contrasting Bayesian and classical methods, specifying hierarchical models, and assessing Markov chain Monte Carlo output. Kate Cowles taught Suzuki piano for many years before going to graduate school in Biostatistics. Her research areas are Bayesian and computational statistics, with application to environmental science. She is on the faculty of Statistics at The University of Iowa.

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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Details

  • NCID
    BB11680291
  • ISBN
    • 9781461456957
  • Country Code
    us
  • Title Language Code
    eng
  • Text Language Code
    eng
  • Place of Publication
    New York
  • Pages/Volumes
    xiv, 232 p.
  • Size
    25 cm
  • Classification
  • Subject Headings
  • Parent Bibliography ID
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