Bayesian Statistical Methods

Explore Banking Institution For Bayesian Statistical Methods

1. ISBN-10

   0815378645
2. ISBN-13

   978-0815378648Bayesian Statistical Methods provides data scientists with the foundational and computational tools necessary to carry out a Bayesian analysis. This publication focuses on Bayesian techniques utilized routinely in practice, including several linear regression, mixed effects models, and generalized linear models (GLM). The authors include numerous examples with complete R code and comparisons with analogous frequentist procedures.

   In addition to the fundamental principles of Bayesian inferential approaches, the publication covers numerous fundamental topics:

    

   • Guidance 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 approaches

   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 methods for large datasets

    

   • Spatial data analysis

   The advanced topics are presented with ample conceptual depth so that the reader will be able to conduct such analysis and debate the relative merits of Bayesian and classical approaches. A repository of R code, motivating data sets, and complete data analyses are available on the publication’s website.

   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 honored with 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.