Bayesian Statistical Methods Chapman & Hall/CRC Texts in Statistical Science Series
Auteurs : Reich Brian J., Ghosh Sujit K.
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.
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.
1. Introduction to Bayesian Inferential Framework. 2. Prior Knowledge to Posterior Inference. 3. Computational Methods. 4. Linear and Generalized Linear Regression Methods. 5. Models for Large Dimensional Parameters. 6. Models for Dependent Data. 7. Models for Data with Irregularities. 8. Models for Infinite Dimensional Parameters. 9. Advanced Computational Methods. 10. Case Studies Using Advanced Bayesian Methods
The code and data is at https://bayessm.wordpress.ncsu.edu/.
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
Date de parution : 06-2021
15.6x23.4 cm
Date de parution : 04-2019
15.2x22.9 cm
Thèmes de Bayesian Statistical Methods :
Mots-clés :
Posterior Probability; Posterior Distribution; Computational Methods; MCMC Sample; Data Analysis; Multiple Linear Regression; Hierarchical Models; Bayesian Lasso; Model Selection; Full Conditional Distributions; Monte Carlo Methods; MCMC Output; Prior Distributions; Small DIC; Markov chain Monte Carlo; MCMC Iteration; R code; Ppd; spatial data analysis; Natural Conjugate Priors; analogous frequentist procedures; Posterior Consistency; Bayesian statistical methods; Marginal Inclusion Probabilities; BF; Conjugate Priors; Gibbs Sampling; Posterior Predictive Checks; Full Conditional; Double Exponential Prior; True Posterior; Normal Normal Model; Joint PMF; Posterior Predictive; Map Estimate; Trace Plots