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Published inJan 2024
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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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3.2 Hierarchical shifts

Proteins are molecules formed by 20 units called amino acids. Each amino acid can appear in a protein 0 or more times. Just as a melody is defined by a sequence of musical notes, a protein is defined by a sequence of amino acids. Some musical note variations can result in small variations of the melody and other variations in completely different melodies. Something similar happens with proteins. One way to study proteins is by using nuclear magnetic resonance (the same technique used for medical imaging). This technique allows us to measure various quantities, one of which is called a chemical shift. You may remember that we saw an example using chemical shifts in Chapter 2.

Suppose we want to compare a theoretical method of computing chemical shift against the experimental observations to evaluate the ability of the theoretical method to reproduce the experimental values. Luckily for us, someone has already run the experiments and carried out the theoretical...

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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