December 2018
Beginner to intermediate
356 pages
11h 50m
English
Overview
This comprehensive book takes you on a journey through Bayesian analysis with Python, showcasing how to use the PyMC3 probabilistic programming library and ArviZ for exploratory analysis. With real-world examples and step-by-step demonstrations, you'll gain a solid understanding of Bayesian statistics and be ready to implement probabilistic models for advanced data analyses.
What this Book will help me do
- Understand the fundamentals of Bayesian inference and its applications.
- Develop probabilistic models using Python tools like PyMC3 and ArviZ.
- Master the practice of evaluating and comparing statistical models.
- Learn to identify and construct appropriate models for various data types.
- Enhance your skills in addressing real-world problems using Bayesian approaches.
Author(s)
Osvaldo Martin is a prominent researcher and educator in Bayesian statistics and computational modeling. He is known for his ability to explain complex mathematical concepts through engaging and accessible writing. With years of experience in statistical research and Python programming, Osvaldo brings both practical and theoretical expertise to his books and tutorials.
Who is it for?
This book is perfect for data scientists, researchers, and developers who want to explore Bayesian analysis and probabilistic programming. It caters to readers new to Bayesian concepts but assumes basic familiarity with Python programming. If you're curious about flexible data modeling and statistical inference, this book is tailored for you.
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