Case Studies in Bayesian Statistical Modelling and Analysis

Case Studies in Bayesian Statistical Modelling and Analysis

Editor(s): Clair L. Alston, Kerrie L. Mengersen, Anthony N. Pettitt

Published Online: 30 OCT 2012 09:00PM EST

Print ISBN: 9781119941828

Online ISBN: 9781118394472

DOI: 10.1002/9781118394472

Series Editor(s): Walter A. Shewhart, Samuel S. Wilks

About this Book

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About The Product

Provides an accessible foundation to Bayesian analysis using real world models

This book aims to present an introduction to Bayesian modelling and computation, by considering real case studies drawn from diverse fields spanning ecology, health, genetics and finance. Each chapter comprises a description of the problem, the corresponding model, the computational method, results and inferences as well as the issues that arise in the implementation of these approaches.

Case Studies in Bayesian Statistical Modelling and Analysis:

  • Illustrates how to do Bayesian analysis in a clear and concise manner using real-world problems.
  • Each chapter focuses on a real-world problem and describes the way in which the problem may be analysed using Bayesian methods.
  • Features approaches that can be used in a wide area of application, such as, health, the environment, genetics, information science, medicine, biology, industry and remote sensing.

Case Studies in Bayesian Statistical Modelling and Analysis is aimed at statisticians, researchers and practitioners who have some expertise in statistical modelling and analysis, and some understanding of the basics of Bayesian statistics, but little experience in its application. Graduate students of statistics and biostatistics will also find this book beneficial.

Table of contents

    1. You have free access to this content
    2. Chapter 1

      Introduction (pages 1–16)

      Clair L. Alston, Margaret Donald, Kerrie L. Mengersen and Anthony N. Pettitt

    3. Chapter 8

      Bayesian Mixed Effects Models (pages 141–158)

      Clair L. Alston, Christopher M. Strickland, Kerrie L. Mengersen and Graham E. Gardner

    4. Chapter 19

      Bayesian Classification and Regression Trees (pages 330–347)

      Rebecca A. O'Leary, Samantha Low Choy, Wenbiao Hu and Kerrie L. Mengersen

    5. Chapter 24

      Issues in Designing Hybrid Algorithms (pages 403–420)

      Jeong E. Lee, Kerrie L. Mengersen and Christian P. Robert

    6. Chapter 25

      A Python Package for Bayesian Estimation Using Markov Chain Monte Carlo (pages 421–460)

      Christopher M. Strickland, Robert J. Denham, Clair L. Alston and Kerrie L. Mengersen

    7. You have free access to this content
    8. You have free access to this content

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