Efficient time/space algorithm to compute rectangular probabilities of multinomial, multivariate hypergeometric and multivariate Pólya distributions

Abstract

The computation of rectangular probabilities of multivariate discrete integer distributions such as the multinomial, multivariate hypergeometric or multivariate Polya distributions is of great interest both for statistical applications and for probabilistic modeling purpose. All these distributions are members of a broader family of multivariate discrete integer distributions for which computationaly efficient approximate methods have been proposed for the evaluation of such probabilities, but with no control over their accuracy. Recently, exact algorithms have been proposed for computing such probabilities, but they are either dedicated to a specific distribution or to very specific rectangular probabilities. We propose a new algorithm that allows to perform the computation of arbitrary rectangular probabilities in the most general case. Its accuracy matches or even outperforms the accuracy exact algorithms when the rounding errors are taken into account. In the worst case, its computational cost is the same as the most efficient exact method published so far, and is much lower in many situations of interest. It does not need any additional storage than the one for the parameters of the distribution, which allows to deal with large dimension/large counting parameter applications at no extra memory cost and with an acceptable computation time, which is a major difference with respect to the methods published so far.