Stanley Lazic
Journal of the Royal Statistical Society Series A (Statistics in Society)
Book Recommendations:
Recommended by Stanley Lazic
“With the number of Bayesian statistics books proliferating, a natural question is ‘what sets this one apart’? First, the authors have a deep understanding of the software as they are contributors and developers of several Bayesian packages in the Python ecosystem. Second, the book covers useful but rarely discussed topics such as Bayesian additive regression trees (BART), fitting models with approximate Bayesian computation (ABC) methods and probabilistic programming languages, which takes a computer science perspective and compares several languages. Third, the book covers not only a wide range of models (splines, hierarchical, time series and state-space models are also discussed) but also provides depth of coverage so that users can apply the methods to their own research. ... The book is ideal for self-study, but end of chapter exercises could make it suitable for an undergraduate course. Some knowledge of Python, probability and fitting models to data are need to fully benefit from the content.” (from Amazon)
Osvaldo A. Martin, Ravin Kumar, Junpeng Lao(you?)
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.