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Likelihood and Non‐parametric Bayesian MCMC Inference for Spatial Point Processes Based on Perfect Simulation and Path Sampling
Author(s) -
Berthelsen Kasper K.,
Møller Jesper
Publication year - 2003
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/1467-9469.00348
Subject(s) - mathematics , inference , markov chain monte carlo , parametric statistics , bayesian inference , sampling (signal processing) , path (computing) , statistical inference , bayesian probability , context (archaeology) , algorithm , statistics , computer science , artificial intelligence , paleontology , filter (signal processing) , computer vision , biology , programming language
We consider the combination of path sampling and perfect simulation in the context of both likelihood inference and non‐parametric Bayesian inference for pairwise interaction point processes. Several empirical results based on simulations and analysis of a data set are presented, and the merits of using perfect simulation are discussed.