Established in 2005 under support of MŠMT ČR (project 1M0572)

Publications

Marginalization Algorithm for Compositional Models.

Typ:
Conference paper
Proceedings name:
Information Processing and Management of Uncertainty in Knowledge-based Systems.
Number of part:
3
Publisher:
Éditions EDK
Serie:
Paris
Year:
2006
Pages:
2300-2307
ISBN:
2-84254-112-X
Keywords:
Compositional model, Multidimensional distribution
Anotation:
The paper deals with a problem of marginalization of multidimensional probability distributions represented by compositional models, more precisely by perfect sequence models. It appears that from the computational point of view, the solution is more efficient than any known marginalization process for Bayesian networks. This is because the process, which is in the paper described in a form of an algorithm, takes advantage of the fact that perfect sequence models have some information explicitly encoded, which can be got from Bayesian networks only by application of rather computationally expensive procedures.
 
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