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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

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Product type Paperback
Published in Nov 2016
Publisher Packt
ISBN-13 9781785883804
Length 282 pages
Edition 1st Edition
Languages
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Author (1):
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Osvaldo Martin Osvaldo Martin
Author Profile Icon Osvaldo Martin
Osvaldo Martin
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Table of Contents (10) Chapters Close

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

Installing the necessary Python packages

The code in the book was written using Python version 3.5, and it is recommended 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, but code could need minor adjustments.

The recommended 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 the following 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

To install the latest stable version of PyMC3, run the following command on a command-line terminal:

pip install pymc3
You have been reading a chapter from
Bayesian Analysis with Python
Published in: Nov 2016
Publisher: Packt
ISBN-13: 9781785883804
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