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Polynomial Constraints in Causal Bayesian Networks


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Kang, Changsung and Tian, Jin (2007) Polynomial Constraints in Causal Bayesian Networks. Technical Report, Computer Science, Iowa State University.

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Abstract

We use the implicitization procedure to generate polynomial equality constraints on the set of distributions induced by local interventions on variables governed by a causal Bayesian network with hidden variables. We show how we may reduce the complexity of the implicitization problem and make the problem tractable in certain causal Bayesian networks. We also show some preliminary results on the algebraic structure of polynomial constraints. The results have applications in distinguishing between causal models and in testing causal models with combined observational and experimental data.

Subjects:Computing Methodologies: ARTIFICIAL INTELLIGENCE: General
ID code:00000531
Deposited by:Changsung Kang on 07 March 2007



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