BAYES-KNOWLEDGE
Effective Bayesian Modelling with Knowledge before Data
| Coordinatore | QUEEN MARY UNIVERSITY OF LONDON
Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie. |
| Nazionalità Coordinatore | United Kingdom [UK] |
| Totale costo | 1˙572˙562 € |
| EC contributo | 1˙572˙562 € |
| Programma | FP7-IDEAS-ERC
Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) |
| Code Call | ERC-2013-ADG |
| Funding Scheme | ERC-AG |
| Anno di inizio | 2014 |
| Periodo (anno-mese-giorno) | 2014-04-01 - 2018-03-31 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 |
QUEEN MARY UNIVERSITY OF LONDON
Organization address
address: 327 MILE END ROAD contact info |
UK (LONDON) | hostInstitution | 1˙572˙562.00 |
| 2 |
QUEEN MARY UNIVERSITY OF LONDON
Organization address
address: 327 MILE END ROAD contact info |
UK (LONDON) | hostInstitution | 1˙572˙562.00 |
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Obiettivo del progetto (Objective)
'This project aims to improve evidence-based decision-making. What makes it radical is that it plans to do this in situations (common for critical risk assessment problems) where there is little or even no data, and hence where traditional statistics cannot be used. To address this problem Bayesian analysis, which enables domain experts to supplement observed data with subjective probabilities, is normally used. As real-world problems typically involve multiple uncertain variables, Bayesian analysis is extended using a technique called Bayesian networks (BNs). But, despite many great benefits, BNs have been under-exploited, especially in areas where they offer the greatest potential for improvements (law, medicine and systems engineering). This is mainly because of widespread resistance to relying on subjective knowledge. To address this problem much current research assumes sufficient data are available to make the expert’s input minimal or even redundant; with such data it may be possible to ‘learn’ the underlying BN model. But this approach offers nothing when there is limited or no data. Even when ‘big’ data are available the resulting models may be superficially objective but fundamentally flawed as they fail to capture the underlying causal structure that only expert knowledge can provide.
Our solution is to develop a method to systemize the way expert driven causal BN models can be built and used effectively either in the absence of data or as a means of determining what future data is really required. The method involves a new way of framing problems and extensions to BN theory, notation and tools. Working with relevant domain experts, along with cognitive psychologists, our methods will be developed and tested experimentally on real-world critical decision-problems in medicine, law, forensics, and transport. As the work complements current data-driven approaches, it will lead to improved BN modelling both when there is extensive data as well as none.'