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Learning Bayesian Networks with the Saiyan Algorithm
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DOI：10.1145/3385655.png)
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Abstract
En 中文
Some structure learning algorithms have proven to be effective in reconstructing hypothetical Bayesian Network graphs from synthetic data. However, in their mission to maximise a scoring function, many become conservative and minimise edges discovered. While simplicity is desired, the output is often a graph that consists of multiple independent subgraphs that do not enable full propagation of evidence. While this is not a problem in theory, it can be a problem in practice. This article examines a novel unconventional associational heuristic called Saiyan, which returns a directed acyclic graph that enables full propagation of evidence. Associational heuristics are not expected to perform well relative to sophisticated constraint-based and score-based learning approaches. Moreover, forcing the algorithm to connect all data variables implies that the forced edges will not be correct at the rate of those identified unrestrictedly. Still, synthetic and realworld experiments suggest that such a heuristic can be competitive relative to some of the well-established constraint-based, score-based and hybrid learning algorithms.
Keywords:
Bayesian networks
directed acyclic graphs
graphical models
structure learning
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