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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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2.3 Posterior-based decisions

Sometimes, describing the posterior is not enough. We may need to make decisions based on our inferences and reduce a continuous estimation to a dichotomous one: yes-no, healthy-sick, contaminated-safe, and so on. For instance, is the coin fair? A fair coin is one with a θ value of exactly 0.5. We can compare the value of 0.5 against the HDI interval. From Figure 2.3, we can see that the HDI goes from 0.03 to 0.7 and hence 0.5 is included in the HDI. We can interpret this as an indication that the coin may be tail-biased, but we cannot completely rule out the possibility that the coin is actually fair. If we want a sharper decision, we will need to collect more data to reduce the spread of the posterior, or maybe we need to find out how to define a more informative prior.

2.3.1 Savage-Dickey density ratio

One way to evaluate how much support the posterior provides for a given value is to compare the ratio of the posterior and prior densities at...

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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