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Volumn 58, Issue 2, 2002, Pages 280-286
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Bayesian prediction of spatial count data using generalized linear mixed models
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Author keywords
Bayesian inference; Generalized linear mixed model; Geostatistics; Informative prior; Langevin Hastings update; Markov chain Monte Carlo; Prediction; Weed intensity
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Indexed keywords
HERBICIDE;
AGRICULTURE;
ALGORITHM;
ARTICLE;
BAYES THEOREM;
BIOMETRY;
METHODOLOGY;
MONTE CARLO METHOD;
PLANT;
PROBABILITY;
STATISTICAL ANALYSIS;
STATISTICAL MODEL;
STATISTICS;
GEOSTATISTICAL ANALYSIS;
MATHEMATICAL MODEL;
WEED;
AGRICULTURE;
ALGORITHMS;
BAYES THEOREM;
BIOMETRY;
DATA INTERPRETATION, STATISTICAL;
HERBICIDES;
LINEAR MODELS;
MARKOV CHAINS;
MONTE CARLO METHOD;
PLANTS;
BAYESIAN NETWORKS;
CHAINS;
INFERENCE ENGINES;
MARKOV PROCESSES;
MONTE CARLO METHODS;
BAYESIAN INFERENCE;
COUNT DATUM;
GENERALIZED LINEAR MIXED MODELS;
GEO-STATISTICS;
INFORMATIVE PRIORS;
LANGEVIN;
LANGEVIN-HASTING UPDATE;
MARKOV CHAIN MONTE CARLO;
MARKOV CHAIN MONTE-CARLO;
WEED INTENSITY;
FORECASTING;
BAYESIAN ANALYSIS;
SAMPLING;
SPATIAL ANALYSIS;
WEED;
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EID: 0035989805
PISSN: 0006341X
EISSN: None
Source Type: Journal
DOI: 10.1111/j.0006-341X.2002.00280.x Document Type: Article |
Times cited : (126)
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References (19)
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