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

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# Thinking Probabilistically

"Probability theory is nothing but common sense reduced to calculation."
- Pierre Simon Laplace

In this chapter, we will learn about the core concepts of Bayesian statistics and some of the instruments in the Bayesian toolbox. We will use some Python code, but this chapter will be mostly theoretical; most of the concepts we will see here will be revisited many times throughout this book. This chapter, being heavy on the theoretical side, may be a little anxiogenic for the coder in you, but I think it will ease the path in effectively applying Bayesian statistics to your problems.

In this chapter, we will cover the following topics:

• Statistical modeling
• Probabilities and uncertainty
• Bayes' theorem and statistical inference
• Single-parameter inference and the classic coin-flip problem
• Choosing priors and why people often don't like them, but should
• Communicating a Bayesian analysis

## 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.

## 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

Publication date : Dec 26, 2018
Length 356 pages
Edition : 2nd Edition
Language : English
ISBN-13 : 9781789341652
Category :
Languages :
Concepts :

## What do you get with eBook?

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

## Product Details

Publication date : Dec 26, 2018
Length 356 pages
Edition : 2nd Edition
Language : English
ISBN-13 : 9781789341652
Category :
Languages :
Concepts :

11 Chapters
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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