Minimum Relative Entropy Inference for Normal and Monte Carlo Distributions
Machine Learning
2020-07-14 v1 Machine Learning
Abstract
We represent affine sub-manifolds of exponential family distributions as minimum relative entropy sub-manifolds. With such representation we derive analytical formulas for the inference from partial information on expectations and covariances of multivariate normal distributions; and we improve the numerical implementation via Monte Carlo simulations for the inference from partial information of generalized expectation type.
Cite
@article{arxiv.2007.06461,
title = {Minimum Relative Entropy Inference for Normal and Monte Carlo Distributions},
author = {Marcello Colasante and Attilio Meucci},
journal= {arXiv preprint arXiv:2007.06461},
year = {2020}
}