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.
journal_name
Biometricsjournal_title
Biometricsauthors
Christensen OF,Waagepetersen Rdoi
10.1111/j.0006-341x.2002.00280.xsubject
Has Abstractpub_date
2002-06-01 00:00:00pages
280-6issue
2eissn
0006-341Xissn
1541-0420journal_volume
58pub_type
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