Interval estimates for posterior probabilities in a multivariate normal classification model

Screening for a Multivariate Mixture Normal Distribution

Numerical Computation of Multivariate Normal Probabilities

The numerical computation of a multivariate normal probability is often a diicult problem. This article describes a transformation that simpliies the problem and places it into a form that allows eecient calculation using standard numerical multiple integration algorithms. Test results are presented that compare implementations of two algorithms that use the transformation, with currently avail...

Parallel Computation of Multivariate Normal Probabilities

We present methods for the computation of multivari-ate normal probabilities on parallel/ distributed systems. After a transformation of the initial integral, an approximation can be obtained using Monte-Carlo or quasi-random methods. We propose a meta-algorithm for asynchronous sampling methods and derive eecient parallel algorithms for the computation of MVN distribution functions, including ...

Alternative Sampling Methods for Estimating Multivariate Normal Probabilities

We study the performance of alternative sampling methods for estimating multivariate normal probabilities through the GHK simulator. The sampling methods are randomized versions of some quasi-Monte Carlo samples (Halton, Niederreiter, Niederreiter-Xing sequences and lattice points) and some samples based on orthogonal arrays (Latin hypercube, orthogonal array and orthogonal array based Latin hy...

Bayesian model choice based on Monte Carlo estimates of posterior model probabilities

Arange of approximatemethods have been proposed formodel choice based onBayesian principles, given the problems involved in multiple integration in multi-parameter problems. Formal Bayesian model assessment is based on prior model probabilities P(M = j) and posterior model probabilities P(M = j |Y ) after observing the data. An approach is outlined here that produces posterior model probabiliti...