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- 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.

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.

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

Download this book in **EPUB** and **PDF** formats

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Publication date :
Dec 26, 2018

Length
356 pages

Edition :
2nd Edition

Language :
English

ISBN-13 :
9781789341652

Category :

Languages :

Concepts :

Preface

1. Thinking Probabilistically

2. Programming Probabilistically

3. Modeling with Linear Regression

4. Generalizing Linear Models

5. Model Comparison

6. Mixture Models

7. Gaussian Processes

8. Inference Engines

9. Where To Go Next?

10. Other Books You May Enjoy

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Nov 19, 2023

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