Abstract

This paper compares acceptance-rejection sampling and methods of Deák, Genz and Schervish for the numerical computation of multivariate normal probabilities. Tests using randomly chosen problems show that the most efficient numerical methods use a transformation developed by Genz (1992). The methods allow moderately accurate multivariate normal probabilities to be quickly computed for problems with a many as ten variables.