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ICPR
2008
IEEE

Improving Bayesian Network parameter learning using constraints

9 years 7 months ago
Improving Bayesian Network parameter learning using constraints
This paper describes a new approach to unify constraints on parameters with training data to perform parameter estimation in Bayesian networks of known structure. The method is general in the sense that any convex constraint is allowed, which includes many proposals in the literature. Driven by a maximum entropy criterion and the Imprecise Dirichlet Model, we present a constrained convex optimization formulation to combine priors, constraints and data. Experiments indicate benefits of this framework.
Cassio Polpo de Campos, Qiang Ji
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICPR
Authors Cassio Polpo de Campos, Qiang Ji
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