Bayesian Statistical Methods

Brian J. Reich

ISBN 10: 1032093188 / ISBN 13: 9781032093185

Published by Chapman And Hall/CRC, 2021

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized linear models (GLM). The authors include many examples with complete R code and comparisons with analogous frequentist procedures.In addition to the basic concepts of Bayesian inferential methods, the book covers many general topics:Advice on selecting prior distributionsComputational methods including Markov chain Monte Carlo (MCMC)Model-comparison and goodness-of-fit measures, including sensitivity to priorsFrequentist properties of Bayesian methodsCase studies covering advanced topics illustrate the flexibility of the Bayesian approach:Semiparametric regressionHandling of missing data using predictive distributionsPriors for high-dimensional regression modelsComputational techniques for large datasetsSpatial data analysisThe advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets, and complete data analyses are available on the book's website.

Seller Inventory # 9781032093185

Bibliographic Details

• Title: Bayesian Statistical Methods
• Publisher: Chapman And Hall/CRC
• Publication Date: 2021
• Language: English
• ISBN 10: 1032093188
• ISBN 13: 9781032093185
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized linear models (GLM). The authors include many examples with complete R code and comparisons with analogous frequentist procedures.

In addition to the basic concepts of Bayesian inferential methods, the book covers many general topics:

• Advice on selecting prior distributions

• Computational methods including Markov chain Monte Carlo (MCMC)

• Model-comparison and goodness-of-fit measures, including sensitivity to priors
  
  
• Frequentist properties of Bayesian methods

Case studies covering advanced topics illustrate the flexibility of the Bayesian approach:

• Semiparametric regression

• Handling of missing data using predictive distributions

• Priors for high-dimensional regression models

• Computational techniques for large datasets

• Spatial data analysis

The advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets, and complete data analyses are available on the book’s website.

About the Author

Brian J. Reich, Associate Professor of Statistics at North Carolina State University, is currently the editor-in-chief of the Journal of Agricultural, Biological, and Environmental Statistics and was awarded the LeRoy & Elva Martin Teaching Award.

Sujit K. Ghosh, Professor of Statistics at North Carolina State University, has over 22 years of research and teaching experience in conducting Bayesian analyses, received the Cavell Brownie mentoring award, and served as the Deputy Director at the Statistical and Applied Mathematical Sciences Institute