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Bayesian Analysis with Python
Bayesian Analysis with Python

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

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Bayesian Analysis with Python

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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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Last updated date : Feb 11, 2025
Publication date : Nov 25, 2016
Length: 282 pages
Edition : 1st
Language : English
ISBN-13 : 9781785889851
Category :
Languages :
Concepts :

What do you get with eBook?

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Product Details

Last updated date : Feb 11, 2025
Publication date : Nov 25, 2016
Length: 282 pages
Edition : 1st
Language : English
ISBN-13 : 9781785889851
Category :
Languages :
Concepts :

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Table of Contents

9 Chapters
1. Thinking Probabilistically - A Bayesian Inference Primer Chevron down icon Chevron up icon
2. Programming Probabilistically – A PyMC3 Primer Chevron down icon Chevron up icon
3. Juggling with Multi-Parametric and Hierarchical Models Chevron down icon Chevron up icon
4. Understanding and Predicting Data with Linear Regression Models Chevron down icon Chevron up icon
5. Classifying Outcomes with Logistic Regression Chevron down icon Chevron up icon
6. Model Comparison Chevron down icon Chevron up icon
7. Mixture Models Chevron down icon Chevron up icon
8. Gaussian Processes Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.4
(10 Ratings)
5 star 60%
4 star 0%
3 star 0%
2 star 0%
1 star 40%
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Luis Junior Aug 21, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Excellent
Subscriber review Packt
Federico Carrone Jun 23, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I loved this book. I recommend you read it after reading "Probabilistic Programming & Bayesian Methods for Hackers by Cam Davidson-Pilon.
Amazon Verified review Amazon
Jean-Michel Patrick May 16, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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!
Amazon Verified review Amazon
Richard BJ Apr 20, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Really useful, worked examples and all.
Amazon Verified review Amazon
Charles Fribourg Aug 22, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
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