Bayesian Statistical Methods

Designed to provide a good balance of theory and computational methods that will appeal to students and practitioners with minimal mathematical and statistical background and no experience in Bayesian statistics to students and practitioners looking for advanced methodologies.

List of contents

1. Introduction to Bayesian Inferential Framework. 2. Prior Knowledge to Posterior Inference. 3. Computational Methods. 4. Linear and Generalized Linear Regression Methods. 5. Models for Large Dimensional Parameters. 6. Models for Dependent Data. 7. Models for Data with Irregularities. 8. Models for Infinite Dimensional Parameters. 9. Advanced Computational Methods. 10. Case Studies Using Advanced Bayesian Methods
The code and data is at https://bayessm.wordpress.ncsu.edu/.

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

Summary

Designed to provide a good balance of theory and computational methods that will appeal to students and practitioners with minimal mathematical and statistical background and no experience in Bayesian statistics to students and practitioners looking for advanced methodologies.

Product details

• Authors: Sujit K. Ghosh, Ghosh Sujit K., Brian J. Reich, Brian J. (N.c. State University) Ghosh Reich, Brian J. Ghosh Reich
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Hardback
• Released: 16.04.2019
• EAN: 9780815378648
• ISBN: 978-0-8153-7864-8
• No. of pages: 288
• Series: Chapman & Hall/CRC Texts in Statistical Science
• Subjects: Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics; Social sciences, law, business > Business > General, dictionaries; REFERENCE / General, MATHEMATICS / Probability & Statistics / General, MATHEMATICS / Applied, Economic statistics, Probability & statistics, Probability and statistics, Econometrics and economic statistics