Markov Point Processes
Point processes are stochastic processes that are used to model events that occur at random intervals relative to the time axis or the space axis. Thus, there are two types of point processes: temporal point processes and spatial point processes. The representation of physical events as point processes is based on two major assumptions. The first is that the physical events must be pointlike in the sense of occupying a small area in the relevant domain. The second is that the events must be discrete entities so that there will be no ambiguity when they occur. For this reason, a point process can be considered as a set of discrete events that occur at well-defined but random points in time or space.