Likelihood and Non‐parametric Bayesian MCMC Inference for Spatial Point Processes Based on Perfect Simulation and Path Sampling
Copyright policy )Likelihood and Non‐parametric Bayesian MCMC Inference for Spatial Point Processes Based on Perfect Simulation and Path Sampling
AbstractWe 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.
- Aalborg University Denmark
Inference from spatial processes, Strauss process, Nonparametric statistical resampling methods, Numerical analysis or methods applied to Markov chains, maximum likelihood estimation, Non-Markovian processes: estimation, dominated coupling from the past
Inference from spatial processes, Strauss process, Nonparametric statistical resampling methods, Numerical analysis or methods applied to Markov chains, maximum likelihood estimation, Non-Markovian processes: estimation, dominated coupling from the past
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