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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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10.9 Monte Carlo standard error

Even if we have a very low and a very high value of ESS. The samples from MCMC are still finite, and thus we are introducing an error in the estimation of the posterior parameters. Fortunately, we can estimate the error, and it is called the Monte Carlo Standard Error (MCSE). The estimation of the MCSE takes into account that the samples are not truly independent of each other. The precision we want in our results is limited by this value. If the MCSE for a parameter is 0.2, it does not make sense to report a parameter as 2.54. Instead, if we repeat the simulation (with a different random seed), we should expect that for 68% of the results, we obtain values in the range 2.54 ± 0.2. Similarly, for 95% of them, we should get values in the range 2.54 ± 0.4. Here, I am assuming the MCSE distributes normally and then using the fact that ≈ 68% of the value of a Gaussian is within one standard deviation and ≈ 95% is within two standard...

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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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