Bayesian tests for machine learning
This project aims to use modern algorithms ("Dirichlet process", "Markov chain Monte Carlo") to apply Bayesian analysis for assessing/comparing algorithms performance in machine learning. We have implemented the tests in R, Python, Julia and STAN. We use Ipython notebooks to illustrate SW use.
The Imprecise Dirichlet Process Statistical Package
Bayesian methods are ubiquitous in many research areas. Nevertheless, the analysis of empirical results is typically performed by frequentist tests. This implies dealing with null hypothesis significance tests and p-values, even though the shortcomings of such methods are well known. The IDP Statistical Package aims to change this perspective by providing Bayesian nonparametric versions of the most used frequentist tests. IDP is based on a Prior Near-Ignorance Dirichlet Process.