A Decomposition Algorithm for Learning Bayesian Networks Based on Scoring Function

Table 4

Results relative to Algorithm 1: missing edges, extra edges, reversed edges, and computation time for Insurance network.

Algorithm (level 𝛼)	𝑛=1000	𝑛=2000	𝑛=5000	𝑛=10000
	(13.0, 2.4, 20.6, 36.0, 2.7031)	(10.0, 1.5, 21.5, 33, 4.1406)	(7.4, 1.3, 16.3, 25.0, 6.9063)	(6.7, 1.1, 12.2, 20.0, 11.6094)
Algorithm 1 (0.01)	(1.0, 1.0, 1.0, 1.0, 1.0000)	(1.0, 1.0, 1.0, 1.0, 1.0000)	(1.0, 1.0, 1.0, 1.0, 1.0000)	(1.0, 1.0, 1.0, 1.0, 1.0000)
Algorithm 1 (0.05)	(1.2, 1.5, 1.0, 1.1, 1.1850)	(1.1, 1.3, 1.0, 1.0, 1.0981)	(1.0, 1.0, 1.3, 1.1, 1.0158)	(1.0, 1.1, 1.1, 1.1, 1.0202)
RAI (0.01)	(1.0, 1.1, 1.0, 1.1, 1.2879)	(1.0, 1.1, 1.0, 1.0, 1.6792)	(1.0, 1.1, 1.1, 1.1, 2.6955)	(1.0, 1.0, 1.0, 1.1, 4.4231)
RAI (0.05)	(1.1, 1.3, 1.0, 1.1, 1.6636)	(1.1, 1.2, 1.0, 1.0, 1.9857)	(1.0, 1.1, 1.2, 1.2, 3.3154)	(1.0, 1.1, 1.1, 1.1, 5.2848)
X-G-Z (0.01)	(1.0, 1.6, 1.0, 1.0, 7.2486)	(1.2, 2.3, 1.2, 1.3, 6.0151)	(1.3, 2.5, 1.3, 1.4, 7.8868)	(1.4, 2.9, 1.7, 1.7, 8.5505)
X-G-Z (0.05)	(1.1, 1.6, 0.9, 1.0, 9.9249)	(1.2, 2.4, 1.1, 1.2, 8.1661)	(1.3, 2.7, 1.4, 1.4, 16.0270)	(1.3, 3.0, 1.9, 1.8, 11.6998)
MMHC (0.01)	(1.2, 0.6, 1.1, 1.1, 1.4798)	(1.4, 1.0, 1.1, 1.2, 2.1321)	(1.4, 1.1, 1.2, 1.2, 3.8258)	(1.3, 1.0, 1.2, 1.2, 6.7052)
MMHC (0.05)	(1.3, 0.6, 1.1, 1.2, 1.9827)	(1.4, 1.1, 1.2, 1.3, 2.5774)	(1.5, 1.2, 1.2, 1.3, 4.8484)	(1.4, 1.1, 1.2, 1.3, 8.1278)