A Markov jump process associated with the matrix-exponential distribution

TL;DR

This paper extends the probabilistic interpretation of matrix-exponential distributions using Markov jump processes, generalizing phase-type distributions and providing methods to revert exponential tilting for better understanding.

Contribution

It introduces a Markov jump process framework for matrix-exponential distributions, broadening their probabilistic interpretation beyond phase-type distributions.

Findings

Probabilistic interpretation via Markov jump processes

Generalization of phase-type distribution interpretation

Method to revert exponential tilting for original distribution

Abstract

Let $f$ be the density function associated to a matrix-exponential distribution of parameters $(\alpha, T,s)$. By exponentially tilting $f$, we find a probabilistic interpretation which generalises the one associated to phase-type distributions. More specifically, we show that for any sufficiently large $\lambda\ge 0$, the function $x\mapsto \left(\int_0^\infty e^{-\lambda r}f(r) dr\right)^{-1}e^{-\lambda x}f(x)$ can be described in terms of a Markov jump process whose generator is tied to $T$. Finally, we show how to revert the exponential tilting in order to assign a probabilistic interpretation to $f$ itself.