Christopher Fonnesbeck
Vanderbilt University Medical Center and Senior Quantitative Analyst, New York Yankees
Book Recommendations:
Recommended by Christopher Fonnesbeck
“From a technical standpoint, the reviewed chapters are excellent. Too often, statistical textbooks are mathematically sound, but lacking in computational sophistication, or vice versa. These chapters are sound on both fronts. My current primary textbook for Bayesian computation is Bayesian Data Analysis, by Gelman et al. which is probably the standard in academia and industry with respect to applied Bayesian methods. Where Martin et al. differentiate themselves from Gelman et al. (and others) is in the incorporation of Python as the computing language used throughout the book…This manuscript has the potential to be a preferred textbook for those looking for a practical introduction to these methods.” (from Amazon)
by Osvaldo A. Martin, Ravin Kumar, Junpeng Lao·You?
by Osvaldo A. Martin, Ravin Kumar, Junpeng Lao·You?
Bayesian Modeling and Computation in Python aims to help beginner Bayesian practitioners to become intermediate modelers. It uses a hands on approach with PyMC3, Tensorflow Probability, ArviZ and other libraries focusing on the practice of applied statistics with references to the underlying mathematical theory. The book starts with a refresher of the Bayesian Inference concepts. The second chapter introduces modern methods for Exploratory Analysis of Bayesian Models. With an understanding of these two fundamentals the subsequent chapters talk through various models including linear regressions, splines, time series, Bayesian additive regression trees. The final chapters include Approximate Bayesian Computation, end to end case studies showing how to apply Bayesian modelling in different settings, and a chapter about the internals of probabilistic programming languages. Finally the last chapter serves as a reference for the rest of the book by getting closer into mathematical aspects or by extending the discussion of certain topics. This book is written by contributors of PyMC3, ArviZ, Bambi, and Tensorflow Probability among other libraries.