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A suggestion for the quantification of precise and bounded probability to quantify epistemic uncertainty in scientific assessments.
Raices Cruz, Ivette; Troffaes, Matthias C M; Sahlin, Ullrika.
Afiliación
  • Raices Cruz I; Centre for Environmental and Climate Science, Lund University, Lund, Sweden.
  • Troffaes MCM; Department of Biology, Lund University, Lund, Sweden.
  • Sahlin U; Department of Mathematical Sciences, Durham University, Durham, UK.
Risk Anal ; 42(2): 239-253, 2022 02.
Article en En | MEDLINE | ID: mdl-35007348
An honest communication of uncertainty about quantities of interest enhances transparency in scientific assessments. To support this communication, risk assessors should choose appropriate ways to evaluate and characterize epistemic uncertainty. A full treatment of uncertainty requires methods that distinguish aleatory from epistemic uncertainty. Quantitative expressions for epistemic uncertainty are advantageous in scientific assessments because they are nonambiguous and enable individual uncertainties to be characterized and combined in a systematic way. Since 2019, the European Food Safety Authority (EFSA) recommends assessors to express epistemic uncertainty in conclusions of scientific assessments quantitatively by subjective probability. A subjective probability can be used to represent an expert judgment, which may or may not be updated using Bayes's rule to integrate evidence available for the assessment and could be either precise or approximate. Approximate (or bounded) probabilities may be enough for decision making and allow experts to reach agreement on certainty when they struggle to specify precise subjective probabilities. The difference between the lower and upper bound on a subjective probability can also be used to reflect someone's strength of knowledge. In this article, we demonstrate how to quantify uncertainty by bounded probability, and explicitly distinguish between epistemic and aleatory uncertainty, by means of robust Bayesian analysis, including standard Bayesian analysis through precise probability as a special case. For illustration, the two analyses are applied to an intake assessment.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Comunicación / Conocimiento Tipo de estudio: Prognostic_studies Idioma: En Revista: Risk Anal Año: 2022 Tipo del documento: Article País de afiliación: Suecia Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Comunicación / Conocimiento Tipo de estudio: Prognostic_studies Idioma: En Revista: Risk Anal Año: 2022 Tipo del documento: Article País de afiliación: Suecia Pais de publicación: Estados Unidos