A flexible Bayesian method is suggested for the pair potential estimation with a high-dimensional parameter space. The method is based on a Bayesian smoothing technique, commonly applied in statistical image analysis. For the calculation of the posterior mode estimator a new Monte Carlo algorithm is developed. The method is illustrated through examples with both real and simulated data, and its extension into truly nonparametric pair potential estimation is discussed.
Publié le : 1999-12-14
Classification:
Bayesian smoothing,
Gibbs processes,
posterior mode estimator,
Markov chain Monte Carlo methods,
Marquardt algorithm,
pair potential function
@article{1143122305,
author = {Heikkinen, Juha and Penttinen, Antti},
title = {Bayesian smoothing in the estimation of the pair potential function of Gibbs point processes},
journal = {Bernoulli},
volume = {5},
number = {6},
year = {1999},
pages = { 1119-1136},
language = {en},
url = {http://dml.mathdoc.fr/item/1143122305}
}
Heikkinen, Juha; Penttinen, Antti. Bayesian smoothing in the estimation of the pair potential function of Gibbs point processes. Bernoulli, Tome 5 (1999) no. 6, pp. 1119-1136. http://gdmltest.u-ga.fr/item/1143122305/