A versatile Markovian point process

Marcel F. Neuts

Journal of Applied Probability · 1979 · 106 citations · 8 references

Concepts

TL;DR

Point processes such as renewal processes of phase type, Markov‑modulated Poisson processes, and certain semi‑Markov processes are special cases of the class considered. The study introduces a versatile class of point processes on the real line closely related to finite‑state Markov processes. The authors provide a systematic framework that extends many existing probability models to these arrival processes. The paper offers computationally tractable formulas for probability distributions, moments, and correlations, and shows that fluctuations, grouping, interruptions, and inhibition by bunch inputs can be modeled within this framework.

Abstract

We introduce a versatile class of point processes on the real line, which are closely related to finite-state Markov processes. Many relevant probability distributions, moment and correlation formulas are given in forms which are computationally tractable. Several point processes, such as renewal processes of phase type, Markov-modulated Poisson processes and certain semi-Markov point processes appear as particular cases. The treatment of a substantial number of existing probability models can be generalized in a systematic manner to arrival processes of the type discussed in this paper. Several qualitative features of point processes, such as certain types of fluctuations, grouping, interruptions and the inhibition of arrivals by bunch inputs can be modelled in a way which remains computationally tractable.

References

8