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• 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.

Students, researchers and data scientists who wish to learn Bayesian data analysis with Python and implement probabilistic models in their day to day projects. Programming experience with Python is essential. No previous statistical knowledge is assumed.

• Understand the essential 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

This book covers the main concepts of Bayesian statistics and how to apply them to data analysis. It is intended for readers without any previous statistical knowledge, but with some experience using Python. The basic elements of Bayesian modeling are introduced using a computational and practical approach. Synthetic and simple real data sets are used to explain each topic and explore the main features of the Bayesian framework. Among the explored models in the book we find the generalized linear models for regression and classification. Mixture models and hierarchical models are also explained. Model selection is discussed in its own chapter and the book ends with a short introduction to non-parametrics models and Gaussian processes. All Bayesian models are implemented using PyMC3, a Python library for probabilistic programming. Many of the main features of PyMC3 are exemplified throughout the text. With this book and the help of Python and PyMC3 you will learn to implement, check and expand Bayesian statistical models to solve a wide array of data analysis problems.