# Bayesian Analysis with Python

 Learn Understand the essentials Bayesian concepts from a practical point of view Learn how to build probabilistic models using the Python library PyMC3 Acquire the skills to sanity-check your models and modify them if necessary Add structure to your models and get the advantages of hierarchical models Find out how different models can be used to answer different data analysis questions When in doubt, learn to choose between alternative models. Predict continuous target outcomes using regression analysis or assign classes using logistic and softmax regression. Learn how to think probabilistically and unleash the power and flexibility of the Bayesian framework The purpose of this book is to teach the main concepts of Bayesian data analysis. We will learn how to effectively use PyMC3, a Python library for probabilistic programming, to perform Bayesian parameter estimation, to check models and validate them. This book begins presenting the key concepts of the Bayesian framework and the main advantages of this approach from a practical point of view. Moving on, we will explore the power and flexibility of generalized linear models and how to adapt them to a wide array of problems, including regression and classification. We will also look into mixture models and clustering data, and we will finish with advanced topics like non-parametrics models and Gaussian processes. With the help of Python and PyMC3 you will learn to implement, check and expand Bayesian models to solve data analysis problems. Simplify the Bayes process for solving complex statistical problems using Python; Tutorial guide that will take the you through the journey of Bayesian analysis with the help of sample problems and practice exercises; Learn how and when to use Bayesian analysis in your applications with this guide. 282 8 hours 27 minutes 9781785883804 25 Nov 2016
 Statistics as a form of modeling Probabilities and uncertainty Single parameter inference Posterior predictive checks Installing the necessary Python packages Summary Exercises
 Probabilistic programming PyMC3 introduction Summarizing the posterior Summary Keep reading Exercises
 Nuisance parameters and marginalized distributions Gaussians, Gaussians, Gaussians everywhere Comparing groups Hierarchical models Summary Keep reading Exercises
 Simple linear regression Robust linear regression Hierarchical linear regression Polynomial regression Multiple linear regression The GLM module Summary Keep reading Exercises
 Logistic regression Multiple logistic regression Discriminative and generative models Summary Keep reading Exercises
 Occam's razor â€“ simplicity and accuracy Regularizing priors Predictive accuracy measures Bayes factors Bayes factors and information criteria Summary Keep reading Exercises
 Mixture models Model-based clustering Continuous mixtures Summary Keep reading Exercises
 Non-parametric statistics Kernel-based models Gaussian processes Summary Keep reading Exercises