Probability and Bayesian Modeling
Author:   Jim Albert (Emeritus Professor at Bowling Green State Uni.) ,  Jingchen Hu
Publisher:   Taylor & Francis Ltd
ISBN:  

9781138492561


Pages:   552
Publication Date:   18 December 2019
Format:   Hardback
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

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Probability and Bayesian Modeling


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Full Product Details

Author:   Jim Albert (Emeritus Professor at Bowling Green State Uni.) ,  Jingchen Hu
Publisher:   Taylor & Francis Ltd
Imprint:   CRC Press
Weight:   1.020kg
ISBN:  

9781138492561


ISBN 10:   1138492566
Pages:   552
Publication Date:   18 December 2019
Audience:   Professional and scholarly ,  General/trade ,  Professional & Vocational ,  General
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

1. Introduction, examples and review. 2. Why Bayes? 3. One-parameter models. 4. Monte Carlo approximation. 5. Normal models. 6. Gibbs sampler. 7. Metropolis-Hastings algorithms, BUGS. 8. Bayesian hierarchical modeling. 9. Multivariate normal models. 10. Bayesian linear regression. 11. Bayesian model comparison, variable selection and model selection. 12. Applications.

Reviews

The book can be used by upper undergraduate and graduate students as well as researchers and practitioners in statistics and data science from all disciplines...A background of calculus is required for the reader but no experience in programming is needed. The writing style of the book is extremely reader friendly. It provides numerous illustrative examples, valuable resources, a rich collection of materials, and a memorable learning experience. ~Technometrics


Author Information

Jim Albert is a Distinguished University Professor of Statistics at Bowling Green State University. His research interests include Bayesian modeling and applications of statistical thinking in sports. He has authored or coauthored several books including Ordinal Data Modeling, Bayesian Computation with R, and Workshop Statistics: Discovery with Data, A Bayesian Approach. Jingchen (Monika) Hu is an Assistant Professor of Mathematics and Statistics at Vassar College. She teaches an undergraduate-level Bayesian Statistics course at Vassar, which is shared online across several liberal arts colleges. Her research focuses on dealing with data privacy issues by releasing synthetic data.

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