Sarah A Strausser, Kelly M Dopke, Destin Groff, Sue Boehmer, Robert P Olympia
Importance of residency applicant factors based on specialty and demographics: a national survey of program directors
BMC medical education
bayesian networks bns kind graphical model formally combines elements graph probability theory bns mathematically statistically rigorous technique allowing user define pictorial representation assumed dependencies influences among set variables deemed relevant particular inferential problem formalism allows one process newly acquired evidence according rules probability calculus applications bns reported various forensic disciplines however seems reluctance consider bns general framework representing evaluating sources uncertainties associated scientific evidence notably bns widely thought essentially numerical method requiring exact numbers high accuracy present paper aims draw readers attention point availability hard numerical data necessary requirement using bns forensic science abstraction quantitative bns known qualitative probabilistic networks qpns sensitivity analyses presented potential applications discussed main difference quantitative counterpart qpns contain qualitative probabilistic relationships instead numerical relations sensitivity analyses consist varying probabilities assigned one variables evaluating effect one variables interest qpns sensitivity analyses appear useful concepts permit one work contexts acute lack numerical data reasoning consistent laws probability nevertheless performed