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London, United Kingdom

Fenton N.,Queen Mary, University of London | Fenton N.,Agena Ltd.
Science and Justice | Year: 2014

It is crucial to identify the most appropriate hypotheses if one is to apply probabilistic reasoning to evaluate and properly understand the impact of evidence. Subtle changes to the choice of a prosecution hypothesis can result in drastically different posterior probabilities to a defence hypothesis from the same evidence. To illustrate the problem we consider a real case in which probabilistic arguments assumed that the prosecution hypothesis "both babies were murdered" was the appropriate alternative to the defence hypothesis "both babies died of Sudden Infant Death Syndrome (SIDS)". Since it would have been sufficient for the prosecution to establish just one murder, a more appropriate alternative hypothesis was "at least one baby was murdered". Based on the same assumptions used by one of the probability experts who examined the case, the prior odds in favour of the defence hypothesis over the double murder hypothesis are 30 to 1. However, the prior odds in favour of the defence hypothesis over the alternative 'at least one murder' hypothesis are only 5 to 2. Assuming that the medical and other evidence has a likelihood ratio of 5 in favour of the prosecution hypothesis results in very different conclusions about the posterior probability of the defence hypothesis. © 2014 Forensic Science Society. Source


Neil M.,Queen Mary, University of London | Neil M.,Agena Ltd. | Marquez D.,Queen Mary, University of London
Engineering Applications of Artificial Intelligence | Year: 2012

We present a hybrid Bayesian network (HBN) framework to model the availability of renewable systems. We use an approximate inference algorithm for HBNs that involves dynamically discretizing the domain of all continuous variables and use this to obtain accurate approximations for the renewal or repair time distributions for a system. We show how we can use HBNs to model corrective repair time, logistics delay times and scheduled maintenance time distributions and combine these with time-to-failure distributions to derive system availability. Example models are presented and are accompanied by detailed descriptions of how repair (renewal) distributions might be modelled using HBNs. © 2010 Elsevier Ltd. All rights reserved. Source


Trademark
AGENA Corporation | Date: 1991-04-08

computer programs relating to electronic image file management and associated manuals sold as a unit.


Trademark
Agena Corporation | Date: 1986-10-14

COMPUTER PROGRAMS RECORDED ON TAPES, CARDS AND DISCS.


Trademark
AGENA Corporation | Date: 1992-07-14

software that expedites movement of data from agency management system software to third party software.

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