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.

• Type: Conference
• Year: 2008
• Where: NIPS
• Authors: Benjamin Yackley, Eduardo Corona, Terran Lane
• Keywords: Bayesian Network, Bayesian Network Structure-search, Information Technology, Many Interesting Problems, NIPS 2008