Aukema, B.H.

Møller, Jesper

Raffa, K.F.

Rasmussen, Jakob Gulddahl

Zhu, J.

We consider statistical and computational aspects of simulation-based Bayesian inference for a multivariate point process which is only observed at sparsely distributed times. For specicity we consider a particular data set which has earlier been analyzed by a discrete time model involving unknown normalizing constants. We discuss the advantages and disadvantages of using continuous time processes compared to discrete time processes in the setting of the present paper as well as other spatial-temporal situations. Keywords: Bark beetle, conditional intensity, forest entomology, Markov chain Monte Carlo, missing data, prediction, spatial-temporal process.We consider statistical and computational aspects of simulation-based Bayesian inference for a multivariate point process which is only observed at sparsely distributed times. For specicity we consider a particular data set which has earlier been analyzed by a discrete time model involving unknown normalizing constants. We discuss the advantages and disadvantages of using continuous time processes compared to discrete time processes in the setting of the present paper as well as other spatial-temporal situations. Keywords: Bark beetle, conditional intensity, forest entomology, Markov chain Monte Carlo, missing data, prediction, spatial-temporal process

Bayesian inference for multivariate point processes observed at sparsely distributed times

http://vbn.aau.dk/files/4751438/R-2006-24.pdf

Bayesian inference for multivariate point processes observed at sparsely distributed times

Abstract

Similar works

Full text

Available Versions

VBN