Probability and Bayesian Modeling 1st Edition

Probability and Bayesian Modeling is a comprehensive textbook designed to introduce undergraduate students with a calculus background to the concepts of probability and Bayesian thinking. The book covers a wide range of topics including foundations of probability, conditional probability, discrete and continuous distributions, joint distributions, Bayesian inference, and statistical modeling.

The authors, Jim Albert and Jingchen (Monika) Hu, provide a modern approach to Bayesian statistical practice by incorporating simulation techniques throughout the text. The book emphasizes the use of simulation to analyze posterior and predictive distributions, and introduces students to Markov Chain Monte Carlo algorithms for Bayesian inference.

Key Features of the Book:

• Introduction to probability and Bayesian thinking
• Statistical inference from a Bayesian perspective
• Simulation techniques in probability and Bayesian modeling
• Bayesian inference for hierarchical and regression models
• Case studies based on historical Bayesian research and real-world applications

Authors:

Jim Albert

Jim Albert is a Distinguished Faculty Professor of Statistics at Bowling Green State University. His research interests include Bayesian modeling and the application of statistical thinking in sports. He is the author of several books, including Ordinal Data Modeling, Bayesian Computation with R, and Workshop Statistics: Discovery with Data, A Bayesian Approach.

Jingchen (Monika) Hu

Jingchen Hu is an Assistant Professor of Mathematics and Statistics at Vassar College. She teaches an undergraduate-level Bayesian Statistics course at Vassar and focuses her research on data privacy issues through the use of synthetic data.

Frequently Asked Questions

What background is required to understand Probability and Bayesian Modeling 1st Edition?

It is recommended that students have a calculus background to fully grasp the concepts presented in the textbook.

Are there practical examples included in the book?

Yes, the book includes case studies based on historical Bayesian research and real-world applications to help illustrate the concepts discussed.

Conclusion

Probability and Bayesian Modeling 1st Edition offers a thorough introduction to probability and Bayesian thinking for undergraduate students. With a focus on modern Bayesian statistical practice and simulation techniques, the book provides a comprehensive overview of the subject. Authored by experts in the field, the textbook is a valuable resource for students looking to deepen their understanding of Bayesian modeling and statistical inference.