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You're reading from  Bayesian Analysis with Python - Third Edition

Product typeBook
Published inJan 2024
Reading LevelExpert
PublisherPackt
ISBN-139781805127161
Edition3rd Edition
Languages
Python
Concepts
Machine Learning
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Author (1)
Osvaldo Martin
Osvaldo Martin
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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.
Read more about Osvaldo Martin

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6.6 Categorical predictors

A categorical variable represents distinct groups or categories that can take on a limited set of values from those categories. These values are typically labels or names that don’t possess numerical significance on their own. Some examples are:

  • Political affiliation: conservative, liberal, or progressive.

  • Sex: female or male.

  • Customer satisfaction level: very unsatisfied, unsatisfied, neutral, satisfied, or very satisfied.

Linear regression models can easily accommodate categorical variables; we just need to encode the categories as numbers. There are a few options to do so. Bambi can easily handle the details for us. The devil is in the interpretation of the results, as we will explore in the next two sections.

6.6.1 Categorical penguins

For the current example, we are going to use the palmerpenguins dataset, Horst et al. [2020], which contains 344 observations of 8 variables. For the moment, we are interested in modeling the mass of the...

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Bayesian Analysis with Python - Third Edition
Published in: Jan 2024Publisher: PacktISBN-13: 9781805127161
© 2024 Packt Publishing Limited All Rights Reserved

Author (1)

author image
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.
Read more about Osvaldo Martin

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