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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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5.8 Regularizing priors

Using informative and weakly informative priors is a way of introducing bias in a model and, if done properly, this can be really good because bias prevents overfitting and thus contributes to models being able to make predictions that generalize well. This idea of adding a bias element to reduce generalization errors without affecting the ability of the model to adequately model a problem is known as regularization. This regularization often takes the form of a term penalizing certain values for the parameters in a model, like too-big coefficients in a regression model. Restricting parameter values is a way of reducing the data a model can represent, thus reducing the chances that a model will capture noise instead of the signal.

This regularization idea is so powerful and useful that it has been discovered several times, including outside the Bayesian framework. For regression models, and outside Bayesian statistics, two popular regularization methods are ridge...

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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