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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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4.11 Exercises

  1. Using the howell dataset (available at https://github.com/aloctavodia/BAP3), create a linear model of the weight (x) against the height (y). Exclude subjects that are younger than 18. Explain the results.

  2. For four subjects, we get the weights (45.73, 65.8, 54.2, 32.59), but not their heights. Using the model from the previous exercise, predict the height for each subject, together with their 50% and 94% HDIs. Tip: Use pm.MutableData.

  3. Repeat exercise 1, this time including those below 18 years old. Explain the results.

  4. It is known for many species that weight does not scale with height, but with the logarithm of the weight. Use this information to fit the howell data (including subjects from all ages).

  5. See the accompanying code model_t2 (and the data associated with it). Experiment with priors for ν, like the non-shifted Exponential and Gamma priors (they are commented on in the code). Plot the prior distribution to ensure that you understand them. An easy...

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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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