Computing the Dirichlet-Multinomial Log-Likelihood Function
Machine Learning
2020-07-24 v1 Artificial Intelligence
Machine Learning
Abstract
Dirichlet-multinomial (DMN) distribution is commonly used to model over-dispersion in count data. Precise and fast numerical computation of the DMN log-likelihood function is important for performing statistical inference using this distribution, and remains a challenge. To address this, we use mathematical properties of the gamma function to derive a closed form expression for the DMN log-likelihood function. Compared to existing methods, calculation of the closed form has a lower computational complexity, hence is much faster without comprimising computational accuracy.
Keywords
Cite
@article{arxiv.2007.11967,
title = {Computing the Dirichlet-Multinomial Log-Likelihood Function},
author = {Djallel Bouneffouf},
journal= {arXiv preprint arXiv:2007.11967},
year = {2020}
}