- Generate synthetic from a mixture of three Gaussians. Check the accompanying Jupyter Notebook for this chapter for an example on how to do this. Fit a finite Gaussian mixture model with 2, 3, or 4 components.
- Use WAIC and LOO to compare the results from exercise 1.
- Read and run the following examples about mixture models from the PyMC3 documentation ( https://pymc-devs.github.io/pymc3/examples):
- Marginalized Gaussian Mixture Model (https://docs.pymc.io/notebooks/marginalized_gaussian_mixture_model.html)
- Dependent density regression (https://docs.pymc.io/notebooks/dependent_density_regression.html)
- Gaussian Mixture Model with ADVI (https://docs.pymc.io/notebooks/gaussian-mixture-model-advi.html) (you will find more information about ADVI in Chapter 8, Inference Engines)
- Repeat exercise 1 using a Dirichlet process.
- Assuming for a moment that you do not know the correct...
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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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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