Learning Bayesian Networks in Semi-deterministic Systems
by Wei Luo
In current constraint-based (Pearl-style) systems for discovering Bayesian
networks, inputs with deterministic relations are prohibited. This restricts the
applicability of these systems. In this talk, I formalize a sufficient condition
under which Bayesian networks can be recovered even with deterministic
relations. The sufficient condition leads to an improvement to Pearl's IC
algorithm; other constraint-based algorithms can be similarly improved. The new
algorithm, assuming the sufficient condition proposed, is able to recover
Bayesian networks with deterministic relations, and moreover suffers no loss of
performance when applied to nondeterministic Bayesian networks.
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