@inbook{0b90d8a08d9011da8c84000ea68e967b,
title = "Bayesian analysis of Markov point processes",
abstract = "Recently M{\o}ller, Pettitt, Berthelsen and Reeves introduced a new MCMC methodology for drawing samples from a posterior distribution when the likelihood function is only specified up to a normalising constant. We illustrate the method in the setting of Bayesian inference for Markov point processes; more specifically we consider a likelihood function given by a Strauss point process with priors imposed on the unknown parameters. The method relies on introducing an auxiliary variable specified by a normalised density which approximates the likelihood well. For the Strauss point process we use a partially ordered Markov point process as the auxiliary variable. As the method requires simulation from the {"}unknown{"} likelihood, perfect simulation algorithms for spatial point processes become useful.",
author = "Berthelsen, {Kasper Klitgaard} and Jesper M{\o}ller",
year = "2006",
language = "English",
isbn = "0387283110",
series = "Lecture Notes in Statistics",
publisher = "Springer",
number = "185",
pages = "85--97",
editor = "Adrian Baddeley and Pablo Gregori and Jorge Mateu and Radu Stoica and Dietrich Stoyan",
booktitle = "Case Studies in Spatial Point Process Modeling",
address = "Germany",
}