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