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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

Who this book is for

Undergraduate or graduate students, scientists, and data scientists who are not familiar with the Bayesian statistical paradigm and wish to learn how to do Bayesian data analysis. No previous knowledge of statistics is assumed, for either Bayesian or other paradigms. The required mathematical knowledge is kept to a minimum and all concepts are described and explained with code, figures, and text. Mathematical formulas are used only when we think it can help the reader to better understand the concepts. The book assumes you know how to program in Python. Familiarity with scientific libraries such as NumPy, matplotlib, or Pandas is helpful but not essential.

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