Perfectly random sampling of truncated multinormal distributions

Perfectly random sampling of truncated multinormal distributions

A “coupling from the past” construction of the Gibbs sampler process is used to perfectly simulate a random vector in a box B, a Cartesian product of bounded intervals. An algorithm to sample vectors with multinormal distribution truncated to B is implemented. AMS Classification 60G15 60G10 65C05

Hessian orders and multinormal distributions

Several well known integral stochastic orders (like the convex order, the supermodular order, etc.) can be defined in terms of the Hessian matrix of a class of functions. Here we consider a generic Hessian order, i.e., an integral stochastic order defined through a convex coneH of Hessian matrices, and we prove that if two random vectors are ordered by the Hessian order, then their means are eq...

Random Distributions, Random Affine Systems, Sampling of Renewal Processes

Expectations of linear functions with respect to truncated multinormal distributions - With applications for uncertainty analysis in environmental modelling

This paper discusses results concerning multivariate normal distributions that are subject to truncation by a hyperplane and how such results can be applied to uncertainty analysis in the environmental sciences. We present a suite of results concerning truncated multivariate normal distributions, some of which already appear in the mathematical literature. The focus here is to make these types ...

Sampling Distributions Sampling Distributions