Tractable Bayesian learning of tree belief networks
✍ Scribed by Marina Meilă; Tommi Jaakkola
- Book ID
- 106537115
- Publisher
- Springer US
- Year
- 2006
- Tongue
- English
- Weight
- 362 KB
- Volume
- 16
- Category
- Article
- ISSN
- 0960-3174
No coin nor oath required. For personal study only.
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Bayesian belief networks provide a natural, efficient method for representing probabilistic dependencies among a set of variables. For these reasons, numerous researchers are exploring the use of belief networks as a knowledge representation m artificial intelligence. Algorithms have been developed
Suermondt, H.J. and G.F. Cooper, Initialization for the method of conditioning in Bayesian belief networks (Research Note), Artificial Intelligence 50 (1991) 83-94. The method of conditioning allows us to use Pearl's probabilistic-inference algorithm in multiply connected belief networks by instant