Dear collegues
I am looking for references about the use of contraints in building
Bayesian Belief Networks.
For example let us suppose to have as prior knowledge (1) a prior network
as a first aproximation of the target network and (2) a set of constraints
on the values of the variables in particular cases. Is it possible to
refine the prior network using the constraints to obtain a new networks
matching such constraints?
One way could be to adopt some learning methods, using the constraints to
create the data base for the learning process; but is there an alternative
way to refine directly the conditional ditributions according to the
constraints?
Many thanks in advance
Yours Sincerely
Roldano Cattoni
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