TY - RPRT
T1 - Bayesian inference for multivariate point processes observed at sparsely distributed times
AU - Rasmussen, Jakob Gulddahl
AU - Møller, Jesper
AU - Aukema, B.H.
AU - Raffa, K.F.
AU - Zhu, J.
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.
M3 - Report
T3 - Research Report Series
BT - Bayesian inference for multivariate point processes observed at sparsely distributed times
PB - Department of Mathematical Sciences, Aalborg University
CY - Aalborg
ER -