Description
Book SynopsisProbability and Bayesian Modeling is an introduction to probability and Bayesian thinking for undergraduate students with a calculus background. The first part of the book provides a broad view of probability including foundations, conditional probability, discrete and continuous distributions, and joint distributions. Statistical inference is presented completely from a Bayesian perspective. The text introduces inference and prediction for a single proportion and a single mean from Normal sampling. After fundamentals of Markov Chain Monte Carlo algorithms are introduced, Bayesian inference is described for hierarchical and regression models including logistic regression. The book presents several case studies motivated by some historical Bayesian studies and the authors' research.
This text reflects modern Bayesian statistical practice. Simulation is introduced in all the probability chapters and extensively used in the Bayesian material to simulate fr
Trade Review
"The book can be used by upper undergraduate and graduate students as well as researchers and practitioners in statistics and data science from all disciplines…A background of calculus is required for the reader but no experience in programming is needed. The writing style of the book is extremely reader friendly. It provides numerous illustrative examples, valuable resources, a rich collection of materials, and a memorable learning experience."
~Technometrics
"Over many years, I have wondered about the following: Should a first undergraduate course in statistics be a Bayesian course? After reading this book, I have come to the conclusion that the answer is…yes!... this is very well written textbook that can also be used as self-learning material for practitioners. It presents a clear, accessible, and entertaining account of the interplay of probability, computations, and statistical inference from the Bayesian perspective."
~ISCB News
Table of Contents1. 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.
