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Can I read Local conditioning in Bayesian networks on EtoBox?
Local conditioning in Bayesian networks by F.J. Díez is a Computer Science article available to read on EtoBox.
What is Local conditioning in Bayesian networks about?
Local conditioning (LC) is an exact algorithm for computing probability in Bayesian networks, developed as an extension of Kim and Pearl's algorithm for singly-connected networks. A list of variables associated to each node guarantees that only the nodes inside a loop are conditioned on the variable which breaks it. The main advantage of this algorithm is that it computes the probability directly on the original network instead of building a cluster tree, and this can save time when debugging a model and when the sparsity of evidence allows a pruning of the network. The algorithm is also advantageous when some families in the network interact through AND/OR gates. A parallel implementation of the algorithm with a processor for each node is possible even in the case of multiply-connected networks.
Who reads Local conditioning in Bayesian networks?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- F.J. Díez
- Publisher
- Elsevier Science; Elsevier ; Elsevier BV (ISSN 0004-3702)
- Published
- 1996
- Language
- EN
- Field
- Computer Science (Physical Sciences)