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

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Published inJan 2024
Reading LevelExpert
PublisherPackt
ISBN-139781805127161
Edition3rd Edition
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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.
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3.5 Hierarchies all the way up

Various data structures lend themselves to hierarchical descriptions that can encompass multiple levels. For example, consider professional football (soccer) players. As in many other sports, players have different positions. We may be interested in estimating some skill metrics for each player, for the positions, and for the overall group of professional football players. This kind of hierarchical structure can be found in many other domains as well:

  • Medical research: Suppose we are interested in estimating the effectiveness of different drugs for treating a particular disease. We can categorize patients based on their demographic information, disease severity, and other relevant factors and build a hierarchical model to estimate the probability of cure or treatment success for each subgroup. We can then use the parameters of the subgroup distribution to estimate the overall probability of cure or treatment success for the entire patient population.

  • Environmental...

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Bayesian Analysis with Python - Third Edition
Published in: Jan 2024Publisher: PacktISBN-13: 9781805127161

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