Jim Albert, Jingchen Hu

Probability and Bayesian Modeling (eBook, PDF)

• Format: PDF

Produktbeschreibung

Bayesian statistics has been advancing in many aspects in recent years. Bayesian learning provides a natural framework for students to solve scientific problems. This book provides an introduction to Bayesian analysis for undergraduate students with calculus, statistics, and a computational background.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 552
• Erscheinungstermin: 6. Dezember 2019
• Englisch
• ISBN-13: 9781351030137
• Artikelnr.: 58354971

Autorenporträt

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.

Inhaltsangabe

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.