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SUMMARY:Using Chain Event Graphs to Address Asymmetric Evidence in Legal R
 easoning - Anjali Mazumder (Carnegie Mellon University\; University of War
 wick)
DTSTART:20160927T103000Z
DTEND:20160927T111500Z
UID:TALK67608@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Co-author: James Q. Smith (University of Warwick)<b><i><br></i
 ></b><br>Bayesian networks (BNs)\, a class of probabilistic graphical mode
 ls\, have been useful in providing a graphical representation of a problem
 \, calculating marginal and conditional probabilities of interest\, and ma
 king inferences particularly addressing propositions about the source or a
 n evidential-sample. To address propositions relating to activities\, ther
 e is a need to account for different plausible explanations of a suspect/p
 erpetrator&rsquo\;s actions and events as it relates to the evidence. We p
 ropose the use of another class of graphical models\, chain event graphs (
 CEGs)\, exploiting event tree structures to depict the unfolding events as
  postulated by each side (defence and prosecution) and differing explanati
 ons/scenarios. Different explanations/scenarios can introduce different se
 ts of relevant information affecting the dependence relationship between v
 ariables and symmetry of the structure. With the use of case examples invo
 lving transfer and persistence and different evidence types (but in which 
 DNA provides a sub-source level of attribution)\, we further show how CEGs
  can assist in the careful pairing and development of propositions and ana
 lysis of the evidence by addressing uncertainty and the asymmetric unfoldi
 ng of the events to better assist the courts.
LOCATION:Seminar Room 1\, Newton Institute
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