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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?
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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

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