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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.
Read more about Osvaldo Martin

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Bibliography

   Oriol Abril-Pla, Virgile Andreani, Colin Carroll, Larry Dong, Christopher J. Fonnesbeck, Maxim Kochurov, Ravin Kumar, Jupeng Lao, Christian C. Luhmann, Osvaldo A. Martin, Michael Osthege, Ricardo Vieira, Thomas Wiecki, and Robert Zinkov. Pymc: A modern and comprehensive probabilistic programming framework in python. PeerJ Computer Science, 9:e1516, 2023. doi: 10.7717/peerj-cs.1516.

   Agustina Arroyuelo, Jorge A. Vila, and Osvaldo A. Martin. Exploring the quality of protein structural models from a bayesian perspective. Journal of Computational Chemistry, 42(21): 1466–1474, 2021. doi: https://doi.org/10.1002/jcc.26556. URL https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.26556.

   Joseph K. Blitzstein. Introduction to Probability 2ed. Chapman and Hall/CRC, Boca Raton, 2 edition edition, February 2019. ISBN 978-1-138-36991-7.

   Luis Jorge Borges. Ficciones. Sur...

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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