TY - RPRT

T1 - Bayesian inference for multivariate point processes observed at sparsely distributed times

A1 - Rasmussen,Jakob Gulddahl

A1 - Møller,Jesper

A1 - Aukema,B.H.

A1 - Raffa,K.F.

A1 - Zhu,J.

AU - Rasmussen,Jakob Gulddahl

AU - Møller,Jesper

AU - Aukema,B.H.

AU - Raffa,K.F.

AU - Zhu,J.

PB - Department of Mathematical Sciences, Aalborg University

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

BT - Bayesian inference for multivariate point processes observed at sparsely distributed times

T3 - Research Report Series

T3 - en_GB

ER -