Bayesian Statistical Methods

ISBN-13: 9781032093185 / Angielski / Miękka / 2021 / 288 str.

Sujit K. Ghosh

Opis

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.

Spis treści

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/.

O autorze

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