Case Studies in Bayesian Statistical Modelling and Analysis

About this book

Case Studies in Bayesian Statistical Modelling and Analysis provides an accessible foundation into Bayesian modelling and analysis using real-world models. Each chapter comprises of 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. Coverage focuses on a real-world problems drawn from the editors' own experiences while illustrating the way in which the problem can be analyzed using Bayesian methods.

Contents

List of Contributors
Contributors
Preface

1 Introduction
2 Overview
3 Further Reading
4 Bayesian Software
5 Applications
6 MCMC for some further applications
7 Zellner’s g-prior for regression models
8 Objective priors
9 Ordering of Hierarchies in Hierarchical Models: Bone Mineral Density Estimation
10 BayesianWeibull Survival Model For Gene Expression Data
11 Bayesian Change Point Detection in Monitoring Clinical Outcomes
12 Bayesian Splines
13 Disease Mapping using Bayesian hierarchical models
14 Moisture, crops and salination: an analysis of a three dimensional agricultural datase
15 A Bayesian Approach to Multivariate State Space Modelling: A Study of a Fama-French Asset Pricing Model with Time Varying Regressors
16 Bayesian mixture models: When the thing you need to know is the thing you cannot measure
17 Latent Class Models in Medicine
18 Hidden Markov models for complex stochastic processes: A case study in electrophysiology
19 Bayesian classification and regression trees
20 Tangled webs: using Bayesian networks in the fight against infection
21 Implementing adaptive dose finding studies using sequential Monte Carlo
22 Likelihood Free Inference for Transmission Rates of Nosocomial Pathogens
23 Variational Bayesian Inference for Mixture Models
24 Issues in designing hybrid algorithms
25 A Python Package for Bayesian Estimation Using Markov Chain Monte Carlo

References
Index

Customer Reviews