title: Bayesian prediction of spatial count data using generalized linear mixed models.
abstract: 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.
authors.affiliation: Department of Mathematical Sciences, Aalborg University, Denmark. olefc@math.auc.dk
authors.forename: Ole F
authors.initials: OF
authors.lastname: Christensen
authors.forename: Rasmus
authors.initials: R
authors.lastname: Waagepetersen
mesh_terms: D000383:Agriculture / Q000379:methods / Q000706:statistics & numerical data; D000465:Algorithms; D001499:Bayes Theorem; D001699:Biometry / Q000379:methods; D003627:Data Interpretation, Statistical; D006540:Herbicides / Q000008:administration & dosage; D016014:Linear Models; D008390:Markov Chains; D009010:Monte Carlo Method; D010944:Plants
chemical_list: D006540:Herbicides