عنوان

Bayesian statistical methods /

شابک

0429202296

شابک

0429510918

شابک

0429514344

شابک

0429517777

شابک

9780429202292

شابک

9780429510915

شابک

9780429514340

شابک

9780429517778

شابک اشتباه

0815378645

شابک اشتباه

9780815378648

عنوان اصلي

Bayesian statistical methods /

نام عام مواد

[Book]

نام نخستين پديدآور

Brian J. Reich, Sujit K. Ghosh

محل نشرو پخش و غیره

Boca Raton :

نام ناشر، پخش کننده و غيره

CRC Press, Taylor & Francis Group,

تاریخ نشرو بخش و غیره

2019

تاریخ نشرو بخش و غیره

©2019

نام خاص و کميت اثر

1 online resource (xii, 275 pages) :

ساير جزييات

illustrations

عنوان فروست

Chapman & Hall/CRC texts in statistical science series

متن يادداشت

Includes bibliographical references and index

متن يادداشت

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

بدون عنوان

0

متن يادداشت

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

منبع سفارش / آدرس اشتراک

Taylor & Francis

شماره انبار

9780429202292

عنوان

Bayesian statistical methods.

شماره استاندارد بين المللي کتاب و موسيقي

9780815378648

موضوع مستند نشده

Bayesian statistical decision theory, Problems, exercises, etc.

موضوع مستند نشده

Mathematical analysis, Problems, exercises, etc.

موضوع مستند نشده

Bayesian statistical decision theory.

موضوع مستند نشده

Mathematical analysis.

موضوع مستند نشده

MATHEMATICS-- Applied.

موضوع مستند نشده

MATHEMATICS-- Probability & Statistics-- General.

موضوع مستند نشده

MAT-- 003000

موضوع مستند نشده

MAT-- 029000

موضوع مستند نشده

PBT

شماره

ويراست

شماره رده

نشانه اثر

مستند نام اشخاص تاييد نشده

Reich, Brian J., (Brian James)

مستند نام اشخاص تاييد نشده

Ghosh, Sujit K.,1970-

تاريخ عمليات

20200822111357.0

قواعد فهرست نويسي ( بخش توصيفي )

pn

نام الکترونيکي

نوع ماده

[Book]

تكميل شده

Y