Think continuous: Markovian Gaussian models in spatial statistics
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Bayesian penalized spline models for the analysis of spatio-temporal count data.Estimating the expected value of partial perfect information in health economic evaluations using integrated nested Laplace approximationLocal overfishing may be avoided by examining parameters of a spatio-temporal model.Comment: Getting into Space with a Weight Problem.Bayesian Computation for Log-Gaussian Cox Processes: A Comparative Analysis of Methods.Spatial models for probabilistic prediction of wind power with application to annual-average and high temporal resolution dataGoing off grid: computationally efficient inference for log-Gaussian Cox processes
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Q27330431-999F3F81-8EDD-4A00-AA76-5B87B0CBFD57Q37272487-DAD1B875-EE10-4E0D-A546-3A0728008E5BQ41491033-93DB2F07-0ADB-499C-899F-0494FD44731DQ42317407-4056A1CD-9B65-4C79-BABF-F7F228DC3B57Q47375882-38477721-3A60-469D-8D22-15CD92C3823CQ53555337-97BA36E8-D8E2-4FA4-9120-59ED68581A42Q57266346-497D3CCC-D3E9-446C-B806-8CB51CF1EFE8
P2860
Think continuous: Markovian Gaussian models in spatial statistics
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wetenschappelijk artikel
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наукова стаття, опублікована в травні 2012
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Think continuous: Markovian Gaussian models in spatial statistics
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Think continuous: Markovian Gaussian models in spatial statistics
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Think continuous: Markovian Gaussian models in spatial statistics
@en
Think continuous: Markovian Gaussian models in spatial statistics
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Think continuous: Markovian Gaussian models in spatial statistics
@en
Think continuous: Markovian Gaussian models in spatial statistics
@nl
P1433
P1476
Think continuous: Markovian Gaussian models in spatial statistics
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P2093
Daniel Simpson
Håvard Rue
P356
10.1016/J.SPASTA.2012.02.003
P50
P577
2012-05-01T00:00:00Z