Add to ORKGPrevious work has shown that the problem of learning the optimal structure of a Bayesian network can be formulated as a shortest path find-ing problem in a graph and solved using A* search. In this paper, we improve the scalabil-ity of this approach by developing a memory-efficient heuristic search algorithm for learning the structure of a Bayesian network. Instead of using A*, we propose a frontier breadth-first branch and bound search that leverages the layered structure of the search graph of this prob-lem so that no more than two layers of the graph, plus solution reconstruction information, need to be stored in memory at a time. To further improve scalability, the algorithm stores most of the graph in external memory, such as hard disk...
It is well known in the literature that the problem of learning the structure of Bayesian networks i...
We describe a memory-efficient implementation of a dynamic programming algorithm for learning the op...
Developing efficient strategies for searching larger Bayesian networks in exact structure learning i...
Learning Bayesian networks is often cast as an optimization problem, where the computational task is...
Several heuristic search algorithms such as A* and breadth-first branch and bound have been develope...
State-of-the-art exact algorithms for solving the MAP problem in Bayesian networks use depth-first b...
A recent breadth-first branch and bound algorithm (BF-BnB) for learning Bayesian network structures ...
A recent breadth-first branch and bound algorithm (BFBnB)for learning Bayesian network structures (M...
Bayesian networks are a widely used graphical model which formalize reasoning under uncertainty. Unf...
This paper formulates learning optimal Bayesian network as a shortest path finding problem. An A* se...
Bayesian network structure learning is NP-hard. Several anytime structure learning algorithms have b...
Bayesian networks are frequently used to model statistical dependencies in data. Without prior knowl...
Early methods for learning a Bayesian network that optimizes a scoring function for a given dataset ...
Bounding the tree-width of a Bayesian network can reduce the chance of overfitting, and allows exact...
Learning Bayesian networks is a central problem for pattern recognition, density estimation and clas...
It is well known in the literature that the problem of learning the structure of Bayesian networks i...
We describe a memory-efficient implementation of a dynamic programming algorithm for learning the op...
Developing efficient strategies for searching larger Bayesian networks in exact structure learning i...
Learning Bayesian networks is often cast as an optimization problem, where the computational task is...
Several heuristic search algorithms such as A* and breadth-first branch and bound have been develope...
State-of-the-art exact algorithms for solving the MAP problem in Bayesian networks use depth-first b...
A recent breadth-first branch and bound algorithm (BF-BnB) for learning Bayesian network structures ...
A recent breadth-first branch and bound algorithm (BFBnB)for learning Bayesian network structures (M...
Bayesian networks are a widely used graphical model which formalize reasoning under uncertainty. Unf...
This paper formulates learning optimal Bayesian network as a shortest path finding problem. An A* se...
Bayesian network structure learning is NP-hard. Several anytime structure learning algorithms have b...
Bayesian networks are frequently used to model statistical dependencies in data. Without prior knowl...
Early methods for learning a Bayesian network that optimizes a scoring function for a given dataset ...
Bounding the tree-width of a Bayesian network can reduce the chance of overfitting, and allows exact...
Learning Bayesian networks is a central problem for pattern recognition, density estimation and clas...
It is well known in the literature that the problem of learning the structure of Bayesian networks i...
We describe a memory-efficient implementation of a dynamic programming algorithm for learning the op...
Developing efficient strategies for searching larger Bayesian networks in exact structure learning i...