A Representation of Uncertainty to Aid Insight into Decision Models

摘要: Many real world models can be characterized as weak, meaning that there is significant uncertainty in both the data input and inferences. This lack of determinism makes it especially difficult for users computer decision aids to understand have confidence models. paper presents a representation utilities serves framework graphical summary computer-generated explanation The application described tests methodology aid designed enhance clinician-patient consultation process patients with angina (chest pain due blood flow heart muscle). model represented Bayesian network. Additionally, probabilities are treated random variables probability distributions on their range possible values. initial represent information all anginal symptoms, approach allows rapid tailoring more patientspecific distributions. provides metric judging importance each variable dynamically.