Assessment, Criticism and Improvement of Imprecise Subjective
  Probabilities for a Medical Expert System
  Three paediatric cardiologists assessed nearly 1000 imprecise subjective
conditional probabilities for a simple belief network representing congenital
heart disease, and the quality of the assessments has been measured using
prospective data on 200 babies. Quality has been assessed by a Brier scoring
rule, which decomposes into terms measuring lack of discrimination and
reliability. The results are displayed for each of 27 diseases and 24
questions, and generally the assessments are reliable although there was a
tendency for the probabilities to be too extreme. The imprecision allows the
judgements to be converted to implicit samples, and by combining with the
observed data the probabilities naturally adapt with experience. This appears
to be a practical procedure even for reasonably large expert systems.
