Bayesian Analysis with Python: Unleash the power and flexibility of the Bayesian framework

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3.4 (10 Ratings)

eBook Feb 2025 282 pages 1st Edition

zł59.99 ~~zł177.99~~

eBook Feb 2025 282 pages 1st Edition

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Key benefits

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.

Description

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.

Who is this book for?

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.

What you will 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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Luis Junior Aug 21, 2024

Excellent

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Federico Carrone Jun 23, 2019

I loved this book. I recommend you read it after reading "Probabilistic Programming & Bayesian Methods for Hackers by Cam Davidson-Pilon.

Amazon Verified review

Jean-Michel Patrick May 16, 2019

A great book, very well written and that will introduce you to the main themes of Bayesian inference. Tackles a breadth of topics that will allow you to start doing amazing - and even complex - Bayesian models.Hands-on with Python and PyMC3 and end-of-chapter exercises, which is very pedagogical and lets you really understand the guts of your models. Definitely recommend it to beginners!

Richard BJ Apr 20, 2018

Really useful, worked examples and all.

Charles Fribourg Aug 22, 2017

It is not easy to find materials for a short introductory course in Bayesian Statistics, especially if you want to use PyMC3, and this book gives you all that. The book is highly practical, and goes much more in-depth than "Bayesian Methods for Hackers" or "Think Bayes". All the codes are in Jupyter notebook on Github so that the students can follow quite easily even without much Python experience. It does not assume too much knowledge in Probability or Statistics, and the pages on this is a bit limit. However, there is a reading list at the end of each chapter for the motivated student. All in all, it is a great book to kick start Bayesian Statistics.

Bayesian Analysis with Python: Unleash the power and flexibility of the Bayesian framework

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