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Bayesian networks are used to illustrate how the probability of hav-
ing a disease can be updated given the results from clinical tests. The
problem of diagnosis, that is of determining whether a certain disease
is present, $D$, or absent, $D'$, based on the result of a medical test, is
discussed. Using statistical methods for medical diagnosis, informa-
tion about the disease and symptoms are collected and the databases
are used to diagnose new patients. How can we evaluate the diagnos-
tic probability represented by Pr($D$\ evidence), where evidence is the
result of a clinical test or tests on a new patient? The object of this ar-
ticle is to answer this question. Using the HUGIN software, diagnostic
probabilities are analyzed using the Bayesian approach.