6533b7dcfe1ef96bd1271eb5

RESEARCH PRODUCT

Bayesian Smoothing in the Estimation of the Pair Potential Function of Gibbs Point Processes

Juha HeikkinenAntti Penttinen

subject

Statistics and ProbabilityMathematical optimizationposterior mode estimatorMarkov chain Monte Carlo methodsMonte Carlo methodBayesian probabilityRejection samplingEstimatorMarkov chain Monte CarloBayesian smoothingGibbs processesHybrid Monte Carlosymbols.namesakeMarquardt algorithmsymbolspair potential functionPair potentialAlgorithmMathematicsGibbs sampling

description

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.

yearjournalcountryeditionlanguage
1999-12-01Bernoulli
https://doi.org/10.2307/3318562