On the Representation of Correlated Exponential Distributions by Phase Type Distributions

TL;DR

This paper develops new phase type distribution representations for correlated bivariate exponential distributions, enabling more efficient modeling of correlated exponential and hyperexponential distributions with applications in queueing theory.

Contribution

It introduces novel phase type representations that require fewer phases for given correlations and extends these methods to generate correlated hyperexponential and Erlang distributions.

Findings

New phase type representations reduce the number of phases needed.

Methods to generate Markovian Arrival Processes from phase type models.

Application to queueing models with correlated inter-arrival and service times.

Abstract

In this paper we present results for bivariate exponential distributions which are represented by phase type distributions. The paper extends results from previous publications [5, 14] on this topic by introducing new representations that require a smaller number of phases to reach some correlation coefficient and introduces different ways to describe correlation between exponentially distributed random variables. Furthermore, it is shown how Markovian Arrival Processes (MAPs) with exponential marginal distribution can be generated from the phase type representations of exponential distributions and how the results for exponential distributions can be applied to define correlated hyperexponential or Erlang distributions. As application examples we analyze two queueing models with correlated inter-arrival and service times.