عنوان

Bayesian statistical methods /

(Number (ISBN

0429202296

(Number (ISBN

0429510918

(Number (ISBN

0429514344

(Number (ISBN

0429517777

(Number (ISBN

9780429202292

(Number (ISBN

9780429510915

(Number (ISBN

9780429514340

(Number (ISBN

9780429517778

Erroneous ISBN

0815378645

Erroneous ISBN

9780815378648

Title Proper

Bayesian statistical methods /

General Material Designation

[Book]

First Statement of Responsibility

Brian J. Reich, Sujit K. Ghosh

Place of Publication, Distribution, etc.

Boca Raton :

Name of Publisher, Distributor, etc.

CRC Press, Taylor & Francis Group,

Date of Publication, Distribution, etc.

2019

Date of Publication, Distribution, etc.

©2019

Specific Material Designation and Extent of Item

1 online resource (xii, 275 pages) :

Other Physical Details

illustrations

Series Title

Chapman & Hall/CRC texts in statistical science series

Text of Note

Includes bibliographical references and index

Text of Note

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

0

Text of Note

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

Source for Acquisition/Subscription Address

Taylor & Francis

Stock Number

9780429202292

Title

Bayesian statistical methods.

International Standard Book Number

9780815378648

Bayesian statistical decision theory, Problems, exercises, etc.

Mathematical analysis, Problems, exercises, etc.

Bayesian statistical decision theory.

Mathematical analysis.

MATHEMATICS-- Applied.

MATHEMATICS-- Probability & Statistics-- General.

MAT-- 003000

MAT-- 029000

PBT

Number

Edition

Class number

Book number

Reich, Brian J., (Brian James)

Ghosh, Sujit K.,1970-

Date of Transaction

20200822111357.0

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y