Uncertainty elicitation and quantification from experts
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| Description: |
Wilson, K
Wednesday 10th June 2020 - 14:35 to 15:05 INI Seminar Room 1 |
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| Created: | 2020-06-11 13:52 |
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| Collection: | Infectious Dynamics of Pandemics: Mathematical and statistical challenges in understanding the dynamics of infectious disease pandemics |
| Publisher: | Isaac Newton Institute |
| Copyright: | Wilson, K |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
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| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | The incorporation of uncertainty in assessments and predictions from mathematical models is critical, especially if the models are to be used to support real-world decisions. In fast moving situations such as a global pandemic of an infectious disease, then data to parameterise models are typically patchy and incomplete, and sufficient suitable data may not exist for many parameters in model runs of possible future scenarios. In such cases expert judgements can play an important role, both to specify uncertainty distributions for parameters with no available data and to supplement data where they are available (via Bayes Theorem, or more informally). In this talk I will discuss the elicitation of uncertainty distributions for individual unknowns from a single expert, the combination of the opinions of multiple experts on an unknown into a single uncertainty distribution and the elicitation of graphical models, with an emphasis on Bayesian networks, to produce suitable model structures from experts over multiple dependent unknowns. I will emphasise a behavioural aggregation approach, the SHeffield Elicitation Framework (SHELF), for the combination of the opinions of multiple experts, which will complement the talk from Prof Aspinall on a mathematical aggregation approach, the Classical Method. A running example on the development of a diagnostic test will be used to illustrate the ideas, and I will try to bring out particular issues surrounding infectious disease modelling. Resources: · A probabilistic judgements e-learning course, aimed at explaining elicitation generally and the Sheffield ELicitation Framework specifically: http://www.tonyohagan.co.uk/shelf/ecourse.htm · A textbook providing comprehensive coverage of elicitation: O'Hagan et al (2006). Uncertain Judgements: Eliciting Experts' Probabilities, Wiley. · Resources to conduct an elicitation using SHELF including slide sets, advice and document templates:http://www.tonyohagan.co.uk/shelf/ · A series of short videos for an online course on Structured Expert Judgment provided by TU Delft (links are near the top of the page): http://rogermcooke.net/ · A textbook discussing the elicitation of probabilistic models: J. Q. Smith (2010). Bayesian decision analysis: principles and practice. Cambridge University Press.
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