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Communication Dans Un Congrès Année : 2000

Top-Down Construction and Repetitive Structures Representation in Bayesian Networks

Résumé

Bayesian networks for large and complex domains are difficult to construct and maintain. For example modifying a small network fragment in a repetitive structure might bevery time consuming. Top-down modelling may simplify the construction of large Bayesian networks, but methods (partly) supporting top-down modelling have only recently been introduced and tools do not exist. In this paper, we try to take a top-down approach to constructing Bayesian networks by using existing object oriented methods. We change these where they fail to support top-down modeling. This provides a new framework that allows top-down methodologies for the construction of Bayesian networks, provides an efficient class hierarchy and a compact way of specifying and representing temporal Bayesian networks. Furthermore, a conceptual simplification is achieved.
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Dates et versions

hal-01573452 , version 1 (09-08-2017)

Identifiants

  • HAL Id : hal-01573452 , version 1

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Olav Bangsø, Pierre-Henri Wuillemin. Top-Down Construction and Repetitive Structures Representation in Bayesian Networks. 13th Florida Artificial Intelligence Research Society Conference, May 2000, Orlando, Florida, United States. pp.282-286. ⟨hal-01573452⟩
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