Gaussian Mixture Models are a classic clustering technique, that can easily be generalised to, for example, semi-supervised learning. Sometimes we need to compute closed-form expressions for the ...

In the world of probability theory and statistics, conditional distribution is an essential concept that helps understand the relationship between two or more events. Conditional distribution provides ...

This package offers an accurate parameter estimation technique for Bayesian Network classifiers. It uses a Hierarchical Dirichlet Process to estimate the parameters (using a collapsed Gibbs sampler).

Abstract: Estimation of conditional distributions is considered. It is assumed that the conditional distribution is either discrete or that it has a density with respect to the Lebesgue measure.

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Abstract: Distributionally robust optimization (DRO) is a powerful tool for decision making under uncertainty. It is particularly appealing because of its ability to leverage existing data. However, ...