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

I am currently updating some algorithms (RVM,HMM) and writing new ipython notebooks, updated version of package should be ready by the end of June 2016. I will also add Latent Dirichlet Allocation to package.

• Linear Models
  • Type II Maximum Likelihood Bayesian Linear Regression code
  • Type II Maximum Likelihood Bayesian Logistic Regression (uses Laplace Approximation) code
  • Variational Bayes Linear Regression code
  • Variational Bayes Logististic Regression (uses local variational bounds) code
• ARD Models
  • Relevance Vector Regression (version 2.0) code
  • Relevance Vector Classifier (version 2.0) code
  • Type II Maximum Likelihood ARD Linear Regression code
  • Type II Maximum Likelihood ARD Logistic Regression code
  • Variational Relevance Vector Regression code
  • Variational Relevance Vector Regression code
• Mixture Models
  • Variational Bayes Gaussian Mixture Model with Automatic Model Selection code, tutorial
  • Variational Bayes Bernoulli Mixture Model code, tutorial
  • 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
• Decomposition Models
  • Latent Dirichlet Allocation (collapsed Gibbs Sampler) code, tutorial

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