Байесовский анализ на Python. Введение в статистическое моделирование и вероятностное программирование с использованием PyMC3 и ArviZ

Bayesian modeling with PyMC3 and exploratory analysis of Bayesian models with ArviZ Key Features A step-by-step guide to conduct Bayesian data analyses using PyMC3 and ArviZ A modern, practical and computational approach to Bayesian statistical modeling A tutorial for Bayesian analysis and best practices with the help of sample problems and practice exercises. Book Description The second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models. The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others. By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to. What you will learn Build probabilistic models using the Python library PyMC3 Analyze probabilistic models with the help of ArviZ Acquire the skills required to sanity check models and modify them if necessary Understand the advantages and caveats of hierarchical models Find out how different models can be used to answer different data analysis questions Compare models and choose between alternative ones Discover how different models are unified from a probabilistic perspective Think probabilistically and benefit from the flexibility of the Bayesian framework Who this book is for If you are a student, data scientist, researcher, or a 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 Explore fundamentals of Bayesian inference and applications of Bayesian modeling for probabilistic machine learning. About This Book * Take a practical approach to Bayesian modeling and explore its best practices using PyMC3 * Perform Bayesian analysis for Gaussian and Markov processes * Build generalized models to solve challenges in classification and regression Who This Book Is For Bayesian Analysis with Python is for you if you are a data scientist, researcher, or developer looking to get started with Bayesian data analysis and probabilistic programming. Although you need not have any previous statistical knowledge, some experience in using Python is expected. What You Will Learn * Build probabilistic models using the Python library PyMC3 * Acquire the skills required to sanity check models and modify them * Understand the advantages of hierarchical models * Find out how different models can be used to answer different data analysis questions * Detect faults in models and choose between alternative models * Discover the connections between statistics and machine learning * Think probabilistically and benefit from the flexibility of the Bayesian framework In Detail The second edition of Bayesian Analysis with Python covers the core concepts of Bayesian statistics and demonstrates how to apply them to data science. The book starts with an introduction to Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library. You'll take a practical computational approach over a mathematical one. Once you've got to grips with the basics, you'll understand synthetic and real datasets, which are used to explain the fundamentals of the Bayesian approach, and be introduced to several types of models such as generalized linear models for regression and classification, mixture models, hierarchical models, and the Gaussian process, among others. By the end of the book, you will have thoroughly studied probabilistic modeling and will be able to design and implement your own Bayesian models with PyMC3 for various data science tasks