Python package for Bayesian Machine Learning with scikit-learn API

Build Status Coverage Status

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Installing & Upgrading package

pip install
pip install --upgrade


  • ARD Models
    • Relevance Vector Regression (version 2.0) code, tutorial
    • Relevance Vector Classifier (version 2.0) code, tutorial
    • Type II Maximum Likelihood ARD Linear Regression code
    • Type II Maximum Likelihood ARD Logistic Regression code, tutorial
    • Variational Relevance Vector Regression code
    • Variational Relevance Vector Classification code, tutorial
  • Decomposition Models
    • Restricted Boltzmann Machines (PCD-k / CD-k, weight decay, adaptive learning rate) code, tutorial
    • Latent Dirichlet Allocation (collapsed Gibbs Sampler) code, tutorial
  • Linear Models
    • Empirical Bayes Linear Regression code, tutorial
    • Empirical Bayes Logistic Regression (uses Laplace Approximation) code, tutorial
    • Variational Bayes Linear Regression code, tutorial
    • Variational Bayes Logististic Regression (uses Jordan local variational bound) code, tutorial
  • Mixture Models
    • Variational Bayes Gaussian Mixture Model with Automatic Model Selection code, tutorial
    • Variational Bayes Bernoulli Mixture Model code, tutorial
    • Dirichlet Process Bernoulli Mixture Model code
    • Dirichlet Process Poisson Mixture Model code
    • Variational Multinoulli Mixture Model code
  • Hidden Markov Models
    • Variational Bayes Poisson Hidden Markov Model code, demo
    • Variational Bayes Bernoulli Hidden Markov Model code
    • Variational Bayes Gaussian Hidden Markov Model code, demo


There are several ways to contribute (and all are welcomed)

 * improve quality of existing code (find bugs, suggest optimization, etc.)
 * implement machine learning algorithm (it should be bayesian; you should also provide examples & notebooks)
 * implement new ipython notebooks with examples 

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