Brian J. Reich, Sujit K. Ghosh

Bayesian Statistical Methods (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 288
• Erscheinungstermin: 12. April 2019
• Englisch
• ISBN-13: 9780429510915
• Artikelnr.: 56841883

Autorenporträt

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

Inhaltsangabe

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/.