Python package for Bayesian Machine Learning with scikit-learn API

Installing & Upgrading package

pip install https://github.com/AmazaspShumik/sklearn_bayes/archive/master.zip
pip install --upgrade https://github.com/AmazaspShumik/sklearn_bayes/archive/master.zip

Algorithms

• 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

Contributions:

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