Minimum Relative Entropy Inference for Normal and Monte Carlo Distributions
23 Pages Posted: 17 Nov 2019 Last revised: 9 Feb 2020
Date Written: February 7, 2020
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.
Keywords: Minimum Relative Entropy, Kullback-Leibler, Hamiltonian Monte Carlo, Flexible Probabilities, Exponential-Family Distributions
JEL Classification: C1, G11
Suggested Citation: Suggested Citation