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 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. 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. 356 10 hours 40 minutes 9781789341652 26 Dec 2018
 Statistics, models, and this book's approach Probability theory Single-parameter inference Communicating a Bayesian analysis Posterior predictive checks Summary Exercises
 Probabilistic programming PyMC3 primer Summarizing the posterior Gaussians all the way down Groups comparison Hierarchical models Summary Exercises
 Simple linear regression Robust linear regression Hierarchical linear regression Polynomial regression Multiple linear regression Variable variance Summary Exercises
 Generalized linear models Logistic regression Multiple logistic regression Poisson regression Robust logistic regression The GLM module Summary Exercises
 Posterior predictive checks Occam's razor – simplicity and accuracy Information criteria Bayes factors Regularizing priors WAIC in depth Summary Exercises
 Mixture models Finite mixture models Non-finite mixture model Continuous mixtures Summary Exercises
 Linear models and non-linear data Modeling functions Gaussian process regression Regression with spatial autocorrelation Gaussian process classification Cox processes Summary Exercises
 Inference engines Non-Markovian methods Markovian methods Diagnosing the samples Summary Exercises