Perfect simulation for interacting point processes, loss networks and Ising models

We present a perfect simulation algorithm for measures that are absolutely continuous with respect to some Poisson process and can be obtained as invariant measures of birth-and-death processes. Examples include area- and perimeter-interacting point processes (with stochastic grains), invariant measures of loss networks, and the Ising contour and random cluster models. The algorithm does not involve couplings of the process with different initial conditions and it is not tied up to monotonicity requirements. Furthermore, it directly provides perfect samples of finite windows of the infinite-volume measure, subjected to time and space "user-impatience bias". The algorithm is based on a two-step procedure: (i) a perfect-simulation scheme for a (finite and random) relevant portion of a (space-time) marked Poisson processes (free birth-and-death process, free loss networks), and (ii) a "cleaning" algorithm that trims out this process according to the interaction rules of the target process. The first step involves the perfect generation of "ancestors" of a given object, that is of predecessors that may have an influence on the birthrate under the target process. The second step, and hence the whole procedure, is feasible if these "ancestors" form a finite set with probability one. We present a sufficiency criteria for this condition, based on the absence of infinite clusters for an associated (backwards) oriented percolation model. The criteria is expressed in terms of the subcriticality of a majorizing multitype branching process, whose corresponding parameter yields bounds for errors due to space-time "user-impatience bias". The approach has previously been used, as an alternative to cluster expansion techniques, to extract properties of the invariant measures involved.