Bayesian Modeling and Computation in Python

NOTE EDITORE

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.

SOMMARIO

Foreword Preface Symbols Chapter 1 Bayesian Inference Chapter 2 Exploratory Analysis of Bayesian Models Chapter 3 Linear Models and Probabilistic Programming Languages Chapter 4 Extending Linear Models Chapter 5 Splines Chapter 6 Time Series Chapter 7 Bayesian Additive Regression Trees Chapter 8 Approximate Bayesian Computation Chapter 9End to End Bayesian Workflows Chapter 10 Probabilistic Programming Languages Chapter 11 Appendiceal Topics Glossary Bibliography Index

AUTORE

Osvaldo A. Martin is a Researcher at IMASL-CONICET in Argentina and the Department of Computer Science from Aalto University in Finland. He has a PhD in biophysics and structural bioinformatics. Over the years he has become increasingly interested in data analysis problems with a Bayesian flavor. He is especially motivated by the development and implementation of software tools for Bayesian statistics and probabilistic modeling. Ravin Kumar is a Data Scientist at Google and previously worked at SpaceX and sweetgreen among other companies. He has an M.S in Manufacturing Engineering and a B.S in Mechanical Engineering. He found Bayesian statistics to be an excellent tool for modeling organizations and informing strategy. This interest in flexible statistical modeling led to a warm welcoming open source community which he is honored to be a member of now. Junpeng Lao is a Data Scientist at Google. Prior to that he did his PhD and subsequently worked as a postdoc in Cognitive Neuroscience. He developed a fondness for Bayesian Statistics and generative modeling after working primarily with Bootstrapping and Permutation during his academic life.

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9780367894368
• Collana: Chapman & Hall/CRC Texts in Statistical Science
• Dimensioni: 10 x 7 in Ø 2.34 lb
• Formato: Copertina rigida
• Illustration Notes: 118 b/w images, 83 color images, 12 tables, 118 line drawings and 83 color line drawings
• Pagine Arabe: 398
• Pagine Romane: xxii