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SUMMARY:Using Bayesian Networks to Analyze What Experts Need to Know (and 
 When they Know Too Much) - William Thompson (University of California\, Ir
 vine)
DTSTART:20160929T151500Z
DTEND:20160929T160000Z
UID:TALK67711@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:What is the proper evidentiary basis for an expert opinion? Wh
 en do  procedures designed to reduce bias (e.g.\, blinding\, sequential un
 masking) hide  too much from the expert\; when do they hide too little? Wh
 en will an expert&rsquo\;s  opinion be enhanced\, and when might it be deg
 raded or biased\, by the expert&rsquo\;s  consideration of contextual info
 rmation? Questions like this are important in a  variety of domains in whi
 ch decision makers rely on expert analysis or opinion.  This presentation 
 will discuss the use of conditional probabilities and Bayesian  networks t
 o analyze these questions\, providing examples from forensic science\,  se
 curity analysis\, and clinical medicine. It will include discussion of the
   recommendations of the U.S. National Commission on Forensic Science on  
 determining the &ldquo\;task-relevance&rdquo\; of information needed for f
 orensic science  assessments.&nbsp\;
LOCATION:Seminar Room 1\, Newton Institute
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