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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

Product type Paperback

Published in Dec 2018

Publisher Packt

ISBN-13 9781789341652

Length 356 pages

Edition 2nd Edition

Languages

Python

Concepts

Data Science

Author (1):

Osvaldo Martin

View More author details

Table of Contents (11) Chapters

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

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Poisson regression

Another very popular generalized linear model is the Poisson regression. This model assumes data is distributed according to the, wait for it... Poisson distribution.

One scenario where Poisson distribution is useful is when counting things, such as the decay of a radioactive nucleus, the number of children per couple, or the number of Twitter followers. What all these examples have in common is that we usually model them using discrete non-negative numbers: {0, 1, 2, 3, ....}. This type of variable receives the name of count data.

Poisson distribution

Imagine we are counting the number of red cars passing through an avenue per hour. We could use Poisson distribution to describe this data. Poisson distribution...

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Authors (1)

Osvaldo Martin

Osvaldo Martin

Osvaldo Martin is a researcher at CONICET, in Argentina. He has experience using Markov Chain Monte Carlo methods to simulate molecules and perform Bayesian inference. He loves to use Python to solve data analysis problems. He is especially motivated by the development and implementation of software tools for Bayesian statistics and probabilistic modeling. He is an open-source developer, and he contributes to Python libraries like PyMC, ArviZ and Bambi among others. He is interested in all aspects of the Bayesian workflow, including numerical methods for inference, diagnosis of sampling, evaluation and criticism of models, comparison of models and presentation of results.

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