Bayesian Analysis with Python: A practical guide to probabilistic modeling

Learn the fundamentals of Bayesian modeling using state-of-the-art Python libraries, such as PyMC, ArviZ, Bambi, and more, guided by an experienced Bayesian modeler who contributes to these libraries Key Features Conduct Bayesian data analysis with step-by-step guidance Gain insight into a modern, practical, and computational approach to Bayesian statistical modeling Enhance your learning with best practices through sample problems and practice exercises Purchase of the print or Kindle book includes a free PDF eBook. Book DescriptionThe third edition of Bayesian Analysis with Python serves as an introduction to the main concepts of applied Bayesian inference and their practical implementation in Python. Utilizing PyMC, a state-of-the-art probabilistic programming library, and ArviZ, a tool for exploratory analysis of Bayesian models, you will also acquaint yourselves with additional Bayesian libraries like Bambi, PreliZ, and Kulprit. In this updated edition, a brief introduction to probability theory concepts enhances your learning journey. The book introduces new topics like Bayesian additive regression trees (BART), variable selection, and prior elicitation, featuring updated examples. Refined explanations, informed by feedback and experience from previous editions, underscore the book's emphasis on Bayesian statistics. Explore various models, including hierarchical models, generalized linear models for regression and classification, mixture models, Gaussian processes, and BART, using synthetic and real datasets. By the end of this book, you will possess a functional understanding of probabilistic modeling, enabling you to design and implement Bayesian models for your own data science challenges. You'll be well-prepared to delve into more advanced material or specialized statistical modeling if the need arises.What you will learn Build probabilistic models using the Python library PyMC Analyze probabilistic models with ArviZ Acquire the skills to sanity-check models and modify them if necessary Understand the advantages and caveats of hierarchical models Learn how different models can answer various data analysis questions Compare models and choose between alternative ones Interpret results and apply your knowledge to real-world problems Explore the unified perspective of various probabilistic models Who this book is forIf you are a student, data scientist, researcher, or developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory, so no previous statistical knowledge is required, although some experience in using Python and NumPy is expected.