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Bayesian Analysis with Python
Bayesian Analysis with Python

Bayesian Analysis with Python: Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ , Second Edition

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Bayesian Analysis with Python

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Key benefits

  • A step-by-step guide to conduct Bayesian data analyses using PyMC3 and ArviZ
  • A modern, practical and computational approach to Bayesian statistical modeling
  • A tutorial for Bayesian analysis and best practices with the help of sample problems and practice exercises.

Description

The second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models. The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others. By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to.

Who is this book for?

If you are a student, data scientist, researcher, or a developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory so no previous statistical knowledge is required, although some experience in using Python and NumPy is expected.

What you will learn

  • Build probabilistic models using the Python library PyMC3
  • Analyze probabilistic models with the help of ArviZ
  • Acquire the skills required to sanity check models and modify them if necessary
  • Understand the advantages and caveats of hierarchical models
  • Find out how different models can be used to answer different data analysis questions
  • Compare models and choose between alternative ones
  • Discover how different models are unified from a probabilistic perspective
  • Think probabilistically and benefit from the flexibility of the Bayesian framework

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Dec 26, 2018
Length: 356 pages
Edition : 2nd
Language : English
ISBN-13 : 9781789349665
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Languages :
Concepts :

What do you get with eBook?

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Product Details

Publication date : Dec 26, 2018
Length: 356 pages
Edition : 2nd
Language : English
ISBN-13 : 9781789349665
Category :
Languages :
Concepts :

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Table of Contents

10 Chapters
Thinking Probabilistically Chevron down icon Chevron up icon
Programming Probabilistically Chevron down icon Chevron up icon
Modeling with Linear Regression Chevron down icon Chevron up icon
Generalizing Linear Models Chevron down icon Chevron up icon
Model Comparison Chevron down icon Chevron up icon
Mixture Models Chevron down icon Chevron up icon
Gaussian Processes Chevron down icon Chevron up icon
Inference Engines Chevron down icon Chevron up icon
Where To Go Next? Chevron down icon Chevron up icon
Other Books You May Enjoy 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 3.2
(17 Ratings)
5 star 41.2%
4 star 5.9%
3 star 5.9%
2 star 23.5%
1 star 23.5%
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N/A Nov 19, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Feefo Verified review Feefo
F. Salvi May 26, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Super Book . Really well written and with interesting examples on the associated github website.
Amazon Verified review Amazon
Daniel C. Thomas Feb 07, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I purchased my copy through packt directly, not through amazon.Wanted to write a positive review because I saw a lot of the negative reviews here that didn't seem fair. This book is really great and the author - and the pymc folks in general - are really helpful and responsive. The errors in typesetting and graph colors, etc, mentioned by others a bit overblown (at least, in my version), and they are almost certainly due to the publishing platform. Packt is really good for fast, hot-topic publication, but they are not famous for typo-free books, etc. I think many of the ones listed by other reviews have already been corrected, and the typos I found were minor. They did not subtract from my ability to learn from this book - I learned a lot, really quickly. If the typos really bother you, you can always run the code on the jupyter notebook, the author is still keeping these up-to-date.Every example I followed (most in the book) worked, especially when I checked my code against the notebooks. I did have to use an earlier (3.7) version of python, which has more to do with where pymc3 is right now - pymc3 didn't play well with my python 3.8 installation.I really wanted to get out of R for my introduction to bayesian methods, and keep things in the scipy ecosystem. This was the only book that was up-to-date and that I found approachable for this. I hope the typos in the book are corrected, but it doesn't really matter, I still find this the best quick-and-dirty intro. And I hope the author writes more books.
Amazon Verified review Amazon
F. Salvi May 26, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book is really well written. The examples are accurate and interesting. The author has immediately answered a couple of my question. I definitely recommend it to learn bayesian analysis wit python.
Amazon Verified review Amazon
J. Lowry Dec 20, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Excellent
Amazon Verified review Amazon
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