Bayesian Statistical Methods
Language: English
Published by CRC Press, 2019
Series: Book 59 of 126 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
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Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 re.
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- Title
- Bayesian Statistical Methods
- Author
- Brian J. Reich (N.C. State University)|Sujit K. Ghosh
- Publisher
- CRC Press
- Publication year
- 2019
- Condition
- New
- Binding
- Gebunden
- Language
- English
- ISBN 10
- 0815378645
- ISBN 13
- 9780815378648
- Series
- Book 59 of 126: Chapman & Hall/CRC Texts in Statistical Science
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.
"Synopsis" may belong to another edition of this title.
About the Author
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
"About the title" may belong to another edition of this title.
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