Bayesian networks (BNs) are causal probabilistic models that combine data and expert knowledge to quantify uncertainty, providing the most rigorous and rational basis for critical decision-making.
We made BNs widely accessible by extending the theory and technology, which was incorporated into a software system (AgenaRisk) that enables non-statisticians to develop and use BN-based systems for high stakes decision-making. One international bank uses our models for routine identification of risk in new and on-going maintenance projects. Since June 2011 our approach has been the focus of an international consortium aimed at addressing the problems of expert witness presentation of Bayesian analyses. In terms of impact on understanding BNs and risk assessment, the publication of the book (Fenton, N.
Developed a range of novel elicitation and modelling techniques that make it much easier for domain experts (who are not trained in Bayesian methods or statistics) to build and use models. Lawyers and forensic scientists have been incorporating our approach to using BNs for evidence evaluation, while the Crown Prosecution Service is considering it to help determine whether or not a prosecution should proceed.


This includes breakthrough work on BN idioms [R2] (which provide common templates for building BNs); and ranked nodes in [R5] (which massively reduces the burden on defining probability tables required for BN models).
A world leading actuarial firm has adopted our approach worldwide for financial risk assessment and regulatory compliance. There has been economic benefit to both Agena and its end-users who include many of the world’s leading technology, defence and transport organisations.
Typical mathematical approaches to risk assessment and decision analysis rely on statistical modelling based on historical data. Yet, theoretical and technical impediments restricted widespread use of BNs; decision-makers who most needed them (in finance, law, medicine, transport and defence) were unable to build and use the models required.
This makes the development of realistic BNs drastically simpler and enables a broader class of risk assessment problems to be solved with much greater accuracy [R3] .
A company responsible for national financial infrastructure uses our models for risk and vulnerability evaluation, while a leading reinsurance company used them for comparing accuracy of competing models for predicting insurance losses arising from catastrophic flood events.


Even for highly constrained problems, with small numbers of variables, it only works with very large volumes of relevant data, and it suffers from the same problem of unobserved factors. A Bayesian network (BN) can capture complex interdependencies between risk factors and effectively combine data with expert judgement to provide rigorous risk quantification and decision support for risk management.
Artificial Intelligence and Law, provides important guidelines to forensic scientists when presenting evidence, based on our experiences of working with forensic scientists and lawyers. Developed breakthrough BN algorithms [R4] that enable modellers to use continuous variables alongside discrete ones, without going through the painful (and intrinsically inaccurate) process of static discretisation.



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