Random sampling from a truncated multivariate normal distribution

tmvtnorm: A Package for the Truncated Multivariate Normal Distribution

Abstract In this article we present tmvtnorm, an R package implementation for the truncated multivariate normal distribution. We consider random number generation with rejection and Gibbs sampling, computation of marginal densities as well as computation of the mean and covariance of the truncated variables. This contribution brings together latest research in this field and provides useful met...

Screening for a Multivariate Mixture Normal Distribution

On Sampling from the Multivariate t Distribution

Abstract The multivariate normal and the multivariate t distributions belong to the most widely used multivariate distributions in statistics, quantitative risk management, and insurance. In contrast to the multivariate normal distribution, the parameterization of the multivariate t distribution does not correspond to its moments. This, paired with a non-standard implementation in the R package...

A new rejection sampling method for truncated multivariate Gaussian random variables restricted to convex sets

Statistical researchers have shown increasing interest in generating truncated multivariate normal distributions. In this paper, we only assume that the acceptance region is convex and we focus on rejection sampling. We propose a new algorithm that outperforms crude rejection method for the simulation of truncated multivariate Gaussian random variables. The proposed algorithm is based on a gene...

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