Likelihood and non-parametric Bayesian MCMC inference for spatial point processes based on perfect simulation and path sampling

被引：22

作者：

Berthelsen, KK ^{[1
]}

Moller, J ^{[1
]}

机构：

[1] Univ Aalborg, Dept Math Sci, DK-9220 Aalborg, Denmark

来源：

SCANDINAVIAN JOURNAL OF STATISTICS | 2003年 / 30卷 / 03期

关键词：

coupling from the past (CFTP); exact simulation; likelihood inference; Markov chain Monte Carlo; multiscale process; non-parametric Bayesian smoothing; pairwise interaction point process; path sampling; simulation-based inference; Strauss process;

D O I：

10.1111/1467-9469.00348

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：549 / 564

页数：16

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