Learning From What You Don't Observe
  The process of diagnosis involves learning about the state of a system from
various observations of symptoms or findings about the system. Sophisticated
Bayesian (and other) algorithms have been developed to revise and maintain
beliefs about the system as observations are made. Nonetheless, diagnostic
models have tended to ignore some common sense reasoning exploited by human
diagnosticians; In particular, one can learn from which observations have not
been made, in the spirit of conversational implicature. There are two concepts
that we describe to extract information from the observations not made. First,
some symptoms, if present, are more likely to be reported before others.
Second, most human diagnosticians and expert systems are economical in their
data-gathering, searching first where they are more likely to find symptoms
present. Thus, there is a desirable bias toward reporting symptoms that are
present. We develop a simple model for these concepts that can significantly
improve diagnostic inference.
