Bayesian networks have been helpful for analyzing the probative value of complex forms of forensic DNA evidence, but some of these network models require experts to estimate the prior probability of specific events. This talk discusses procedures that might be used for elicitation of priors with an eye toward minimizing bias and error. As an illustration it uses a model proposed by Biedermann, Taroni & Thompson (2011) to deal with situations in which the "inclusion" of the suspect as a possible contributor to a mixed DNA sample depends on the value of an unknown variable. (Biedermann, A., Taroni, F. & Thompson, W.C. Using graphical probability analysis (Bayes nets) to evaluate a conditional DNA inclusion. Law, Probability and Risk, 10: 89-121, 2011).
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