Case Studies in Bayesian Statistical Modelling and Analysis ( Wiley Series in Probability and Statistics )

Publication series ：Wiley Series in Probability and Statistics

Author： Clair L. Alston  

Publisher： John Wiley & Sons Inc‎

Publication year： 2012

E-ISBN: 9781118394496

P-ISBN(Hardback):  9781119941828

Subject： O212.8 Bayesian statistics

Language： ENG

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Description

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.

Chapter

Contents

pp.：  1 – 7

Preface

pp.：  7 – 19

List of contributors

pp.：  19 – 21

1 Introduction

pp.：  21 – 25

2 Introduction to MCMC

pp.：  25 – 41

3 Priors: Silent or active partners of Bayesian inference?

pp.：  41 – 54

4 Bayesian analysis of the normal linear regression model

pp.：  54 – 90

5 Adapting ICU mortality models for local data: A Bayesian approach

pp.：  90 – 114

6 A Bayesian regression model with variable selection for genome-wide association studies

pp.：  114 – 127

7 Bayesian meta-analysis

pp.：  127 – 142

8 Bayesian mixed effects models

pp.：  142 – 165

9 Ordering of hierarchies in hierarchical models: Bone mineral density estimation

pp.：  165 – 183

10 Bayesian Weibull survival model for gene expression data

pp.：  183 – 195

11 Bayesian change point detection in monitoring clinical outcomes

pp.：  195 – 210

12 Bayesian splines

pp.：  210 – 221

13 Disease mapping using Bayesian hierarchical models

pp.：  221 – 245

14 Moisture, crops and salination: An analysis of a three-dimensional agricultural data set

pp.：  245 – 264

15 A Bayesian approach to multivariate state space modelling: A study of a Fama–French asset-pricing model with time-varying regressors

pp.：  264 – 276

16 Bayesian mixture models: When the thing you need to know is the thing you cannot measure

pp.：  276 – 291

17 Latent class models in medicine

pp.：  291 – 311

18 Hidden Markov models for complex stochastic processes: A case study in electrophysiology

pp.：  311 – 334

19 Bayesian classification and regression trees

pp.：  334 – 354

20 Tangled webs: Using Bayesian networks in the fight against infection

pp.：  354 – 372

21 Implementing adaptive dose finding studies using sequential Monte Carlo

pp.：  372 – 385

22 Likelihood-free inference for transmission rates of nosocomial pathogens

pp.：  385 – 398

23 Variational Bayesian inference for mixture models

pp.：  398 – 412

24 Issues in designing hybrid algorithms

pp.：  412 – 427

25 A Python package for Bayesian estimation using Markov chain Monte Carlo

pp.：  427 – 445

Index

pp.：  445 – 485

Wiley Series in Probability and Statistics

pp.：  485 – 489

LastPages

pp.：  489 – 499

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