Bayesian Analysis with Python

This book, "Bayesian Analysis with Python," introduces the practical applications of Bayesian techniques for statistical computing and machine learning. You will explore the theoretical aspects of Bayesian inference, while learning to implement these principles using Python and the PyMC3 library to solve real-world data analysis problems.

What this Book will help me do

• Master the principles of Bayesian inference to enhance your statistical analysis skills.
• Develop practical knowledge for creating and refining probabilistic models using PyMC3.
• Understand how to structure advanced models like hierarchical or generalized linear models.
• Apply Bayesian algorithms to common data challenges like regression, classification, and clustering.
• Enhance decision-making capabilities with advanced topics such as Gaussian processes and non-parametric models.

Author(s)

The author of this book is an experienced data scientist and Python programmer with an expertise in Bayesian statistics. They have a strong background in probabilistic programming, which allows them to combine conceptual clarity with practical examples. Their focus is on empowering readers to apply Bayesian methodologies to solve complex analytical problems.

Who is it for?

The book is written for data scientists, statisticians, and analysts who have basic Python programming experience and are looking to learn Bayesian statistical modeling techniques. Whether you're a graduate student, researcher, or industry professional, this book will help you transition to leveraging Bayesian methods in your work.