Markov properties of cluster processes

It is shown that a Poisson cluster point process is a nearest-neighbour Markov point process if the clusters have uniformly bounded diameter, and when the parent Poisson process is replaced by a Markov or nearest-NEighbourMarkovpoint process, the resulting cluster process is also nearest-Neighbourmarkov, provided all clusters are non-empty.

Abstract

We show that a Poisson cluster point process is a nearest-neighbour Markov point process [2] if the clusters have uniformly bounded diameter. It is typically not a finite-range Markov point process in the sense of Ripley and Kelly [12]. Furthermore, when the parent Poisson process is replaced by a Markov or nearest-neighbour Markov point process, the resulting cluster process is also nearest-neighbour Markov, provided all clusters are non-empty. In particular, the nearest-neighbour Markov property is preserved when points of the process are independently randomly translated, but not when they are randomly thinned.