A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution.

ABSTRACT: The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Po

SUBMITTER: Inouye D 

PROVIDER: S-EPMC5624559 | biostudies-literature | 2017 May-Jun

REPOSITORIES: biostudies-literature