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Bayesian Prediction of Spatial Count Data Using Generalized Linear Mixed Models
Author(s) -
Christensen Ole F.,
Waagepetersen Rasmus
Publication year - 2002
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2002.00280.x
Subject(s) - markov chain monte carlo , computer science , sampling (signal processing) , bayesian probability , prior probability , data set , gibbs sampling , generalized linear mixed model , count data , bayesian inference , set (abstract data type) , statistics , algorithm , mathematics , artificial intelligence , machine learning , poisson distribution , filter (signal processing) , computer vision , programming language
Summary. Spatial weed count data are modeled and predicted using a generalized linear mixed model combined with a Bayesian approach and Markov chain Monte Carlo. Informative priors for a data set with sparse sampling are elicited using a previously collected data set with extensive sampling. Furthermore, we demonstrate that so‐called Langevin‐Hastings updates are useful for efficient simulation of the posterior distributions, and we discuss computational issues concerning prediction.