Probabilistic Logic Inference for a Directed Cycle in Bayesian Belief Networks
Abstract
Processing of a directed cycle remains an open question of the Bayesian belief network theory. We propose a message-passing algorithm for initial propagation in the cycle. The results of this algorithms are fully compatible with the results of the algorithms we proposed earlier that is based on another principle. Besides, as result of this computations is formed a semantically equal image of the directed cycle in a Bayesian belief network. For this image we could perform probabilistic logic inference: reconciliation, a priori inference and a posteriori inference.References
Published
2007-08-01
How to Cite
Tulupyev, & Abramyan,. (2007). Probabilistic Logic Inference for a Directed Cycle in Bayesian Belief Networks. SPIIRAS Proceedings, (4), 87-118. https://doi.org/10.15622/sp.4.5
Section
Articles
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