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Bayesian Analysis with Python

You're reading from Bayesian Analysis with Python Unleash the power and flexibility of the Bayesian framework

Product type Paperback

Published in Nov 2016

Publisher Packt

ISBN-13 9781785883804

Length 282 pages

Edition 1st Edition

Languages

Python

Concepts

Data Science

Author (1):

Osvaldo Martin

View More author details

Table of Contents (10) Chapters

Preface

1. Thinking Probabilistically - A Bayesian Inference Primer FREE CHAPTER

2. Programming Probabilistically – A PyMC3 Primer

3. Juggling with Multi-Parametric and Hierarchical Models

4. Understanding and Predicting Data with Linear Regression Models

5. Classifying Outcomes with Logistic Regression

6. Model Comparison

7. Mixture Models

8. Gaussian Processes

Index

What you need for this book

This book is written for Python version >= 3.5, and it is recommended that you use the most recent version of Python 3 that is currently available, although most of the code examples may also run for older versions of Python, including Python 2.7 with minor adjustments.

Maybe the easiest way to install Python and Python libraries is using Anaconda, a scientific computing distribution. You can read more about Anaconda and download it from https://www.continuum.io/downloads. Once Anaconda is in our system, we can install new Python packages with this command: conda install NamePackage.

We will use the following python packages:

Ipython 5.0
NumPy 1.11.1
SciPy 0.18.1
Pandas 0.18.1
Matplotlib 1.5.3
Seaborn 0.7.1
PyMC3 3.0

The rest of the chapter is locked

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