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Efficient Markov Network Structure Discovery Using Independence Tests
We present two algorithms for learning the structure of a Markov network from data: GSMN* and GSIMN.
Toward Provably Correct Feature Selection in Arbitrary Domains
An optimal solution to the problem is a Markov boundary, which is a minimal set of features that make the probability distribution of a target variable conditionally invariant to the state of all other features in the domain.
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