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NIPS
2008

Bayesian Network Score Approximation using a Metagraph Kernel

11 years 1 months ago
Bayesian Network Score Approximation using a Metagraph Kernel
Many interesting problems, including Bayesian network structure-search, can be cast in terms of finding the optimum value of a function over the space of graphs. However, this function is often expensive to compute exactly. We here present a method derived from the study of reproducingkernel Hilbert spaces which takes advantage of the regular structure of the space of all graphs on a fixed number of nodes to obtain approximations to the desired function quickly and with reasonable accuracy. We then test this method on both a small testing set and a real-world Bayesian network; the results suggest that not only is this method reasonably accurate, but that the BDe score itself varies quadratically over the space of all graphs.
Benjamin Yackley, Eduardo Corona, Terran Lane
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2008
Where NIPS
Authors Benjamin Yackley, Eduardo Corona, Terran Lane
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