Estimating posterior probabilities
So, at least in theory, the Bayesian Decision framework completely solves the problem of classification, assuming that the P(i) can be calculated, or at least estimated.
(b) Calculate the posterior probabilities for the parent nodes of the utility node, using a
standard probabilistic inference algorithm.
(c) Calculate the resulting utility for the action.
Return the action with the highest utility.
Example of a Bayesian network in the CDSS context is the Iliad system which makes use of Bayesian reasoning to calculate posterior probabilities of possible diagnoses depending on the symptoms provided.
Speech recognition is a giant calculation of posterior probabilities from evidence. ... At the same time, logical AI tradition has broadened to include probability theory.
Because we compute the location of each case from our prior knowledge of the values for that case on the variables in the model, these probabilities are called posterior probabilities.
 See also: Classification, Distribution, Knowledge, Neural network, Density
  
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