1st Edition
Foundations of Bayesian Statistics for Data Scientists With R and Python
1. Introduction to Bayesian Statistics
2. Bayesian Inference for Proportions
3. Bayesian Inference for Means
4. Bayesian Inference for Linear Models
5. Bayesian Inference for Generalized Linear Models
6. Bayesian MCMC Posterior Computation and Diagnostics
7. Choosing and Extending Bayesian Models
Appendix A Using R for Bayesian Data Analysis Appendix
Appendix B Using Python in Statistical Science
Appendix C Solutions to Exercises (odd-numbered)
Biography
Alan Agresti, Distinguished Professor Emeritus at the University of Florida, is the author of seven books, and has presented short courses in 35 countries. His awards include an honorary doctorate from De Montfort University (UK) and Statistician of the Year from the American Statistical Association (Chicago chapter).
Maria Kateri, Professor of Statistics and Data Science at the RWTH Aachen University. She has long-term experience in teaching statistics courses to students of Data Science, Mathematics, Statistics, Computer Science, Business Administration, and Engineering.
Ranjini Grove is a Teaching Professor in the Department of Statistics at the University of Washington. Since receiving her doctoral degree at Cornell University, she has also held faculty appointments at Brown University and at the University of Florida. In 2022 she was a finalist for a distinguished teaching award at the University of Washington. Since taking a break from academia to be a stay-at-home mom, she has been devoting much energy towards creating an inclusive, learner-centered, and engaging classroom.
Antonietta Mira is a Professor of Statistics at the Università della Svizzera italiana and Insubria University. She is a Fellow of both the International Society for Bayesian Analysis and the Institute of Mathematical Statistics, elected member of the International Statistical Institute, and a recipient of the Savage Award for outstanding doctoral dissertations in Bayesian econometrics and statistics. Her research focuses on Bayesian learning and computing, with a strong interdisciplinary approach. She is also passionate about science communication through books, exhibitions, and mathematical magic conference shows.
