Computing the most likely configuration of a set of variables, at least in the BN community, is known as computing the Maximum a Posteriori hypothesis, or MAP. If all of the nodes in your model are either observed or ones you want to maximize over, then it is a special case of MAP often called Most Probable Explanation, or MPE. Assuming that you have a model already, there are a variety of inference algorithms that will solve it for you. A web search on MPE turns up a number of papers on it. If instead, you are attacking it from a learning perspective, trying to generate an especially good model for producing accurate MAP configurations relative to some training data, I am not aware of any algorithms developed specifically for that purpose. Hope that helps, J.D. Park
On Friday, August 9, 2002, at 12:39 PM, DENVER H DASH wrote: > Hi all, > > I'm looking for references, or even some vocabulary, addressing > or describing the problem of classification with multiple, non-disjoint > and possibly related class variables. > > For example, in a medical diagnosis BN classifier with several possible > disease nodes: the disease nodes are not mutually exclusive, so the > classification problem is to decide which joint configuration of class > variable states is most likely, rather than which state of a single > class > variable is most likely. > > I'd be grateful for any pointers to this problem. > > Cheers, > Denver. >
