January 2024
Beginner
394 pages
8h 51m
English
Overview
Bayesian Analysis with Python is a comprehensive guide to Bayesian statistical modeling and probabilistic programming. Using tools like PyMC, ArviZ, and Bambi, you will learn to build, analyze, and interpret advanced probabilistic models from the ground up, all within a Python environment.
What this Book will help me do
- Master the use of modern Bayesian tools like PyMC and Bambi for probabilistic modeling.
- Learn to effectively analyze Bayesian models with ArviZ to gain actionable insights.
- Understand techniques for prior and posterior checks to improve model robustness.
- Build hierarchical models and explore their real-world applicability.
- Gain the ability to design and implement Bayesian models for various data science challenges.
Author(s)
Osvaldo Martin is an experienced data scientist and researcher who specializes in probabilistic programming and Bayesian methods. With years of expertise in using tools like PyMC, he brings a hands-on approach to statistical modeling. Martin is dedicated to making Bayesian statistics accessible and practical for applied users.
Who is it for?
This book is intended for data scientists, researchers, and software developers who wish to incorporate Bayesian methods in their projects. Aimed at beginners, it assumes no prior statistical knowledge while expecting familiarity with Python and libraries like NumPy. It caters to learners seeking to understand Bayesian reasoning and apply it to solve practical problems.
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