Grid-Enabled Bayesian Network

Yi-feng Zeng, Guo-quan Liu, and Kim-leng Poh

Bayesian network has been a successful tool in the decision support systems. In the changing world, the decision making demands adaptive Bayesian methods that are composed of Bayesian inferential and learning approaches. To achieve this goal, we propose a kind of grid-enabled Bayesian networks that intend to gridify Bayesian inferential and learning methods when the advanced grid computing techniques are integrated. Most of our effort is put into the discussion of grid-enabled learning methods and grid-enabled inferential methods as well as their challenging work on the integration. It is argued that grid-enabled Bayesian networks are able to utilize all available resources to support the adaptive decision making in the changing world.

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