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Learning and Acting with Bayes Nets
20.1 Learning Bayes Nets
The problem of learning a Bayes network is the problem of finding a network that best matches (according to some scoring metric) a training set of data, Ξ, where Ξ is a set of instances of values for all (or at least some) of the variables. By “finding a network,” we mean finding both the structure of the DAG and the conditional probability tables (CPTs) associated with each node in the DAG.
20.1.1 Known Network Structure
If we knew the structure of the network, we have only to find the CPTs. Let’s describe that case first. Often human experts can come up with the appropriate structure for a problem domain but not the CPTs. And learning the CPTs is still needed in the case in which we ...
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