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

7.10 Exercises

  1. Generate synthetic data from a mixture of 3 Gaussians. Check the accompanying Jupyter notebook for this chapter for an example of how to do this. Fit a finite Gaussian mixture model with 2, 3, or 4 components.

  2. Use LOO to compare the results from exercise 1.

  3. Read and run through the following examples about mixture models from the PyMC documentation:

  4. Refit fish_data using a NegativeBinomial and a Hurdle NegativeBinomial model. Use rootograms to compare these two models with the Zero-Inflated Poisson model shown in this chapter.

  5. Repeat exercise 1 using a Dirichlet process.

  6. Assuming for a moment that you do not know the correct species/labels for the iris dataset, use a mixture model to cluster the...

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