Bayesian Prediction of Spatial Count Data Using Generalized Linear Mixed Models
- 1 June 2002
- journal article
- Published by Oxford University Press (OUP) in Biometrics
- Vol. 58 (2) , 280-286
- https://doi.org/10.1111/j.0006-341x.2002.00280.x
Abstract
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.Keywords
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