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Bayesian prediction of spatial count data using generalized linear mixed models. (English) Zbl 1209.62156

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.

MSC:

62J12 Generalized linear models (logistic models)
62F15 Bayesian inference
62P12 Applications of statistics to environmental and related topics
65C40 Numerical analysis or methods applied to Markov chains
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