- 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...
Bayesian Analysis with Python - Second Edition
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Bayesian Analysis with Python - Second Edition
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Overview of this book
The second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models.
The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others.
By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to.
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Current Title:
Bayesian Analysis with Python - Second Edition
Table of Contents (11 chapters)
Preface
Free Chapter
Thinking Probabilistically
Programming Probabilistically
Modeling with Linear Regression
Generalizing Linear Models
Model Comparison
Mixture Models
Gaussian Processes
Inference Engines
Where To Go Next?
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