|
" Bayesian statistical methods / "
Brian J. Reich, Sujit K. Ghosh
| Document Type
|
:
|
BL
|
| Record Number
|
:
|
877819
|
| Main Entry
|
:
|
Reich, Brian J., (Brian James)
|
| Title & Author
|
:
|
Bayesian statistical methods /\ Brian J. Reich, Sujit K. Ghosh
|
| Publication Statement
|
:
|
Boca Raton :: CRC Press, Taylor & Francis Group,, 2019
|
|
|
:
|
, ©2019
|
| Series Statement
|
:
|
Chapman & Hall/CRC texts in statistical science series
|
| Page. NO
|
:
|
1 online resource (xii, 275 pages) :: illustrations
|
| ISBN
|
:
|
0429202296
|
|
|
:
|
: 0429510918
|
|
|
:
|
: 0429514344
|
|
|
:
|
: 0429517777
|
|
|
:
|
: 9780429202292
|
|
|
:
|
: 9780429510915
|
|
|
:
|
: 9780429514340
|
|
|
:
|
: 9780429517778
|
|
|
:
|
0815378645
|
|
|
:
|
9780815378648
|
| Bibliographies/Indexes
|
:
|
Includes bibliographical references and index
|
| Contents
|
:
|
Basics of Bayesian inference -- From prior information to posterior inference -- Computational approaches -- Linear models -- Model selection and diagnostics -- Case studies using hierarchical modeling -- Statistical properties of Bayesian methods
|
| Abstract
|
:
|
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
|
| Subject
|
:
|
Bayesian statistical decision theory, Problems, exercises, etc.
|
| Subject
|
:
|
Mathematical analysis, Problems, exercises, etc.
|
| Subject
|
:
|
Bayesian statistical decision theory.
|
| Subject
|
:
|
Mathematical analysis.
|
| Subject
|
:
|
MATHEMATICS-- Applied.
|
| Subject
|
:
|
MATHEMATICS-- Probability Statistics-- General.
|
| Dewey Classification
|
:
|
519.5/42
|
| LC Classification
|
:
|
QA279.5.R445 2019eb
|
| Added Entry
|
:
|
Ghosh, Sujit K.,1970-
|
| |