System and method of employing efficient operators for bayesian network search
First Claim
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1. A method for learning a Bayesian network, comprising:
- specifying a search space that provides for searching over equivalence classes of the Bayesian network;
employing a set of at least one operator relative to an equivalence class state representation in the search space; and
searching though the representation by scoring the at least one operator locally with a decomposable scoring criteria.
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Abstract
Methods and systems are disclosed for learning Bayesian networks. The approach is based on specifying a search space that enables searching over equivalence classes of the Bayesian network. A set of one or more operators are applied to a representation of the equivalence class. A suitable search algorithm searches in the search space by scoring the operators locally with a decomposable scoring criteria. To facilitate application of the operators and associated scoring, validity tests can be performed to determine whether a given operator is valid relative to the current state representation.
48 Citations
69 Claims
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1. A method for learning a Bayesian network, comprising:
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specifying a search space that provides for searching over equivalence classes of the Bayesian network;
employing a set of at least one operator relative to an equivalence class state representation in the search space; and
searching though the representation by scoring the at least one operator locally with a decomposable scoring criteria. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 28)
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24. A method for learning a Bayesian network, comprising:
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specifying a search space that provides for searching over equivalence classes of the Bayesian network;
employing a set of at least one operator applicable to an equivalence class state representation in the search space;
determining whether operators in the set of at least one operator can validly be applied to the state representation based on a validity condition associated with the respective operator, and searching though the representation by scoring valid operators locally with a decomposable scoring criteria to determine which operator to apply to the state representation to implement a state change from the state representation to a next state representation corresponding to a non-empty equivalence class. - View Dependent Claims (25, 26, 27, 29)
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30. A method for learning a Bayesian network, comprising:
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providing an equivalence-class state representation corresponding to a class of Bayesian network structures in a search space; and
searching through the state representation by computing scores corresponding to changes in the state representation associated with a plurality of operators defined in the search space, each of the scores being computed as a local function on a set of adjacency nodes associated with applying a respective operator to the state representation. - View Dependent Claims (31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52)
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53. A search system for learning a Bayesian network, comprising:
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a current equivalence-class state representation corresponding to a class of Bayesian network structures in a search space;
a set of at least one operator operative to transform the current state representation to a next state representation, the at least one operator having an associated validity condition that defines whether the at least one operator is valid for the current state representation; and
a scoring function that computes a local score associated with the at least one operator relative to the current state representation by employing a score-equivalent and decomposable scoring criteria. - View Dependent Claims (54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69)
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Specification