Summary and Info
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. Content: Chapter 1 Introduction (pages 1–16): Clair L. Alston, Margaret Donald, Kerrie L. Mengersen and Anthony N. PettittChapter 2 Introduction to MCMC (pages 17–29): Anthony N. Pettitt and Candice M. HincksmanChapter 3 Priors: Silent or Active Partners of Bayesian Inference? (pages 30–65): Samantha Low ChoyChapter 4 Bayesian Analysis of the Normal Linear Regression Model (pages 66–89): Christopher M. Strickland and Clair L. AlstonChapter 5 Adapting ICU Mortality Models for Local Data: A Bayesian Approach (pages 90–102): Petra L. Graham, Kerrie L. Mengersen and David A. CookChapter 6 A Bayesian Regression Model with Variable Selection for Genome?Wide Association Studies (pages 103–117): Carla Chen, Kerrie L. Mengersen, Katja Ickstadt and Jonathan M. KeithChapter 7 Bayesian Meta?Analysis (pages 118–140): Jegar O. Pitchforth and Kerrie L. MengersenChapter 8 Bayesian Mixed Effects Models (pages 141–158): Clair L. Alston, Christopher M. Strickland, Kerrie L. Mengersen and Graham E. GardnerChapter 9 Ordering of Hierarchies in Hierarchical Models: Bone Mineral Density Estimation (pages 159–170): Cathal D. Walsh and Kerrie L. MengersenChapter 10 Bayesian Weibull Survival Model for Gene Expression Data (pages 171–185): Sri Astuti Thamrin, James M. McGree and Kerrie L. MengersenChapter 11 Bayesian Change Point Detection in Monitoring Clinical Outcomes (pages 186–196): Hassan Assareh, Ian Smith and Kerrie L. MengersenChapter 12 Bayesian Splines (pages 197–220): Samuel Clifford and Samantha Low ChoyChapter 13 Disease Mapping Using Bayesian Hierarchical Models (pages 221–239): Arul Earnest, Susanna M. Cramb and Nicole M. WhiteChapter 14 Moisture, Crops and Salination: An Analysis of a Three?Dimensional Agricultural Data Set (pages 240–251): Margaret Donald, Clair L. Alston, Rick Young and Kerrie L. MengersenChapter 15 A Bayesian Approach to Multivariate State Space Modelling: A Study of a Fama–French Asset?Pricing Model with Time?Varying Regressors (pages 252–266): Christopher M. Strickland and Philip GharghoriChapter 16 Bayesian Mixture Models: When the Thing You Need to Know is the Thing You Cannot Measure (pages 267–286): Clair L. Alston, Kerrie L. Mengersen and Graham E. GardnerChapter 17 Latent Class Models in Medicine (pages 287–309): Margaret Rolfe, Nicole M. White and Carla ChenChapter 18 Hidden Markov Models for Complex Stochastic Processes: A Case Study in Electrophysiology (pages 310–329): Nicole M. White, Helen Johnson, Peter Silburn, Judith Rousseau and Kerrie L. MengersenChapter 19 Bayesian Classification and Regression Trees (pages 330–347): Rebecca A. O'Leary, Samantha Low Choy, Wenbiao Hu and Kerrie L. MengersenChapter 20 Tangled Webs: Using Bayesian Networks in the Fight Against Infection (pages 348–360): Mary Waterhouse and Sandra JohnsonChapter 21 Implementing Adaptive dose Finding Studies Using Sequential Monte Carlo (pages 361–373): James M. McGree, Christopher C. Drovandi and Anthony N. PettittChapter 22 Likelihood?Free Inference for Transmission Rates of Nosocomial Pathogens (pages 374–387): Christopher C. Drovandi and Anthony N. PettittChapter 23 Variational Bayesian Inference for Mixture Models (pages 388–402): Clare A. McGroryChapter 24 Issues in Designing Hybrid Algorithms (pages 403–420): Jeong E. Lee, Kerrie L. Mengersen and Christian P. RobertChapter 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
More About the Author
Walter Andrew Shewhart (pronounced like "shoe-heart", March 18, 1891 – March 11, 1967) was an American physicist, engineer and statistician, sometimes known as the father of statistical quality control and also related to the Shewhart cycle.
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