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dc.contributor.author
Mikkola, Petrus  
dc.contributor.author
Martín, Osvaldo Antonio  
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Chandramoul, Suyog  
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Hartmann, Marcelo  
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Abril Pla, Oriol  
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Thomas, Owen  
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Pesonen, Henri  
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Corander, Jukka  
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Vehtari, Aki  
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Kaski, Samuel  
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Bürkner, Paul Christian  
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Klami, Arto  
dc.date.available
2023-01-03T16:25:44Z  
dc.date.issued
2021-12  
dc.identifier.citation
Mikkola, Petrus; Martín, Osvaldo Antonio; Chandramoul, Suyog ; Hartmann, Marcelo ; Abril Pla, Oriol ; et al.; Prior knowledge elicitation: The past, present, and future; Cornell University; arXiv; 12-2021; 1-60  
dc.identifier.issn
2331-8422  
dc.identifier.uri
http://hdl.handle.net/11336/183197  
dc.description.abstract
Specification of the prior distribution for a Bayesian model is a central part of the Bayesian workflow for data analysis, but it is often difficult even for statistical experts. Prior elicitation transforms domain knowledge of various kinds into well-defined prior distributions, and offers a solution to the prior specification problem, in principle. In practice, however, we are still fairly far from having usable prior elicitation tools that could significantly influence the way we build probabilistic models in academia and industry. We lack elicitation methods that integrate well into the Bayesian workflow and perform elicitation efficiently in terms of costs of time and effort. We even lack a comprehensive theoretical framework for understanding different facets of the prior elicitation problem.Why are we not widely using prior elicitation? We analyze the state of the art by identifying a range of key aspects of prior knowledge elicitation, from properties of the modelling task and the nature of the priors to the form of interaction with the expert. The existing prior elicitation literature is reviewed and categorized in these terms. This allows recognizing under-studied directions in prior elicitation research, finally leading to a proposal of several new avenues to improve prior elicitation methodology.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Cornell University  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
prior elicitation  
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prior distribution  
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informative prior  
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Bayesian workflow  
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domain knowledge  
dc.subject.classification
Estadística y Probabilidad  
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Matemáticas  
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS  
dc.title
Prior knowledge elicitation: The past, present, and future  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2022-10-04T14:39:48Z  
dc.journal.pagination
1-60  
dc.journal.pais
Estados Unidos  
dc.description.fil
Fil: Mikkola, Petrus. Aalto University; Finlandia  
dc.description.fil
Fil: Martín, Osvaldo Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; Argentina. Aalto University; Finlandia  
dc.description.fil
Fil: Chandramoul, Suyog. Aalto University; Finlandia  
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Fil: Hartmann, Marcelo. University of Helsinki; Finlandia  
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Fil: Abril Pla, Oriol. University of Helsinki; Finlandia  
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Fil: Thomas, Owen. University of Oslo; Noruega  
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Fil: Pesonen, Henri. University of Oslo; Noruega  
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Fil: Corander, Jukka. University of Oslo; Noruega  
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Fil: Vehtari, Aki. Aalto University; Finlandia  
dc.description.fil
Fil: Kaski, Samuel. Aalto University; Finlandia  
dc.description.fil
Fil: Bürkner, Paul Christian. University Of Stuttgart; Alemania  
dc.description.fil
Fil: Klami, Arto. University of Helsinki; Finlandia  
dc.journal.title
arXiv  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/2112.01380