A Linear Time, and Constant Space, Algorithm to Compute the Mixed Moments of the Multivariate Normal Distributions
Abstract
Using recurrences gotten from the Apagodu-Zeilberger Multivariate Almkvist-Zeilberger algorithm we present a linear-time, and constant-space, algorithm to compute the general mixed moments of the k-variate general normal distribution, with any covariance matrix, for any specific k. Besides their obvious importance in statistics, these numbers are also very significant in enumerative combinatorics, since they count in how many ways, in a species with k different genders, a bunch of individuals can all get married, keeping track of the different kinds of heterosexual marriages. We completely implement our algorithm (with an accompanying Maple package, MVNM.txt) for the bivariate and trivariate cases (and hence taking care of our own 2-sex society and a putative 3-sex society), but alas, the actual recurrences for larger k took too long for us to compute. We leave them as computational challenges.
Keywords
Cite
@article{arxiv.2202.09900,
title = {A Linear Time, and Constant Space, Algorithm to Compute the Mixed Moments of the Multivariate Normal Distributions},
author = {Shalosh B. Ekhad and Doron Zeilberger},
journal= {arXiv preprint arXiv:2202.09900},
year = {2022}
}
Comments
5 pages. Exclusively published in the Personal Journal of Shalosh B. Ekhad and Doron Zeilberger and this arxiv. Accompanied by a Maple package and output files available from https://sites.math.rutgers.edu/~zeilberg/mamarim/mamarimhtml/mvnm.html