Reader small image

You're reading from  Bayesian Analysis with Python - Third Edition

Product typeBook
Published inJan 2024
Reading LevelExpert
PublisherPackt
ISBN-139781805127161
Edition3rd Edition
Languages
Right arrow
Author (1)
Osvaldo Martin
Osvaldo Martin
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

Right arrow

3.7 Exercises

  1. Using your own words explain the following concepts in two or three sentences:

    • Complete pooling

    • No pooling

    • Partial pooling

  2. Repeat the exercise we did with model_h. This time, without a hierarchical structure, use a flat prior such as Beta(α = 1,β = 1). Compare the results of both models.

  3. Create a hierarchical version of the tips example from Chapter 2, by partially pooling across the days of the week. Compare the results to those obtained without the hierarchical structure.

  4. For each subpanel in Figure 3.7, add a reference line representing the empirical mean value at each level, that is, the global mean, the forward mean, and Messi’s mean. Compare the empirical values to the posterior mean values. What do you observe?

  5. Amino acids are usually grouped into categories such as polar, non-polar, charged, and special. Build a hierarchical model similar to cs_h but including a group effect for the amino acid category. Compare the results to those...

lock icon
The rest of the page is locked
Previous PageNext Page
You have been reading a chapter from
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