Add to ORKGKnowledge and assumptions behind most Bayesian network models are often not clear to anyone other than their developers. This limits their use as decision support tools in clinical and legal domains where the outcomes of decisions can be critical. We propose a framework for representing knowledge supporting or conflicting with BN, and knowledge associated with factors that are relevant but excluded from the BN. The aim of this framework is to enable domain experts and potential users to browse, review, criticise and modify a BN model without having deep technical knowledge about BNs.Co-authors: Zane Perkins (Queen Mary University of London), Nigel Tai (The Royal London Hospital), William Marsh (Queen Mary University of London
A Bayesian network is a graphical model that encodes probabilistic relationships among variables of ...
Bayesian networks are mathematically and statistically rigorous techniques for handling uncertainty....
Bayesian Belief Networks are a powerful tool for combining different knowledge sources with various ...
There is poor uptake of prognostic decision support models by clinicians regardless of their accurac...
Most documented Bayesian network (BN) applications have been built through knowledge elicitation fro...
Bayesian networks (BNs) are powerful tools that are increasingly being used by forensic and legal ex...
Bayesian networks (BNs) are tools for representing expert knowledge or evidence. They are especially...
Bayesian belief networks (BNs) are well-suited to capturing vague and uncertain knowledge. However, ...
PhDEvidence based medicine (EBM) is defined as the use of best available evidence for decision makin...
Bayesian networks (BNs) are increasingly being used to model environmental systems, in order to: int...
The graphical structure of a Bayesian network (BN) makes it a technology well-suited for developing ...
This thesis investigates the use of Bayesian Networks (BNs), augmented by the Dynamic Dis- cretizati...
Thesis (Ph.D.)--University of Washington, 2015Bayesian networks (BNs) are compact, powerful represen...
Bayesian networks (BNs) are increasingly used to model environmental systems, in order to: integrate...
Bayesian networks (BN) are a valid method to analyze causal dependencies with uncertainties and to c...
A Bayesian network is a graphical model that encodes probabilistic relationships among variables of ...
Bayesian networks are mathematically and statistically rigorous techniques for handling uncertainty....
Bayesian Belief Networks are a powerful tool for combining different knowledge sources with various ...
There is poor uptake of prognostic decision support models by clinicians regardless of their accurac...
Most documented Bayesian network (BN) applications have been built through knowledge elicitation fro...
Bayesian networks (BNs) are powerful tools that are increasingly being used by forensic and legal ex...
Bayesian networks (BNs) are tools for representing expert knowledge or evidence. They are especially...
Bayesian belief networks (BNs) are well-suited to capturing vague and uncertain knowledge. However, ...
PhDEvidence based medicine (EBM) is defined as the use of best available evidence for decision makin...
Bayesian networks (BNs) are increasingly being used to model environmental systems, in order to: int...
The graphical structure of a Bayesian network (BN) makes it a technology well-suited for developing ...
This thesis investigates the use of Bayesian Networks (BNs), augmented by the Dynamic Dis- cretizati...
Thesis (Ph.D.)--University of Washington, 2015Bayesian networks (BNs) are compact, powerful represen...
Bayesian networks (BNs) are increasingly used to model environmental systems, in order to: integrate...
Bayesian networks (BN) are a valid method to analyze causal dependencies with uncertainties and to c...
A Bayesian network is a graphical model that encodes probabilistic relationships among variables of ...
Bayesian networks are mathematically and statistically rigorous techniques for handling uncertainty....
Bayesian Belief Networks are a powerful tool for combining different knowledge sources with various ...