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Bayesian Analysis with Python

You're reading from   Bayesian Analysis with Python Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789341652
Length 356 pages
Edition 2nd 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 (11) Chapters Close

Preface 1. Thinking Probabilistically 2. Programming Probabilistically FREE CHAPTER 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

Where To Go Next?

"Statistician is the technical term for a cynical data scientist."
- Jim Savage

I wrote this book to introduce the main concepts and practices of Bayesian statistics to those who are already familiar with Python and the Python data stack, but not very familiar with statistical analysis. Having read the previous eight chapters, you should have a reasonable practical understanding of many of the main topics of Bayesian statistics. Although you will not be an expert-Bayesian-ninja-hacker (whatever that could be), you should be able to create your own probabilistic models to solve your own data analysis problems. If you are really into Bayesian statistics, this book will not be enough probably no single book will be enough. To become more fluent in Bayesian statistics, you will need practice, time, patience, enthusiasm, and more practice, and you...

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