Bayesian hierarchical models can infer interpretable predictions of leaf area index from heterogeneous datasets

DC FieldValueLanguage
dc.contributor.authorStojanović, Olivera
dc.contributor.authorSiegmann, Bastian
dc.contributor.authorJarmer, Thomas
dc.contributor.authorPipa, Gordon
dc.contributor.authorLeugering, Johannes
dc.date.accessioned2022-04-19T14:02:20Z-
dc.date.available2022-04-19T14:02:20Z-
dc.date.issued2021
dc.identifier.urihttps://osnascholar.ub.uni-osnabrueck.de/handle/unios/51413-
dc.publisherCold Spring Harbor Laboratory
dc.relation.ispartofbioRxiv
dc.rightscc-by
dc.subjectFeature selection
dc.subjectBayesian probability
dc.subjectInference
dc.subjectPattern recognition (psychology)
dc.subjectData mining
dc.subjectCovariate
dc.subjectHierarchical database model
dc.subjectComputer science
dc.subjectInterpretability
dc.subjectStatistical model
dc.subjectMachine learning
dc.subjectBayesian inference
dc.subjectArtificial intelligence
dc.titleBayesian hierarchical models can infer interpretable predictions of leaf area index from heterogeneous datasets
dc.typejournal article
dc.identifier.doihttps://doi.org/10.1101/2021.09.20.461084
dc.identifier.externalhttps://openalex.org/W3201041752
dcterms.oaStatustrue
local.import.sourcefileopenalex_uos_20220409.ris
crisitem.author.deptFB 06 - Mathematik/Informatik-
crisitem.author.deptInstitut für Kognitionswissenschaft-
crisitem.author.deptidfb06-
crisitem.author.deptidinstitute28-
crisitem.author.orcid0000-0002-4652-1640-
crisitem.author.orcid0000-0002-3416-2652-
crisitem.author.parentorgUniversität Osnabrück-
crisitem.author.parentorgFB 08 - Humanwissenschaften-
crisitem.author.grandparentorgUniversität Osnabrück-
crisitem.author.netidJaTh054-
crisitem.author.netidPiGo340-
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