A Threat Assessment Method for Unmanned Aerial Vehicle Based on Bayesian Networks under the Condition of Small Data Sets

Table 8

Parameters of node V learned by 100 samples.

			3000-MLE	100-MLE	100-MCE
Ac(1, 2, 3)	Ty(1, 2, 3)	Ai(1, 2)	V(1, 2, 3)	V(1, 2, 3)	V(1, 2, 3)
1	1	1	(0.1036, 0.2091, 0.6873)	(0, 0.3333, 0.6667)	(0.0981, 0.2135, 0.6884)
1	1	2	(0.1652, 0.4986, 0.3362)	(0, 0.6000, 0.4000)	(0.0753, 0.4437, 0.4810)
1	2	1	(0.3115, 0.3242, 0.3643)	(0, 0.6667, 0.3333)	(0.2375, 0.4423, 0.3202)
1	2	2	(0.0129, 0.1357, 0.8514)	(0.3333, 0.3333, 0.3333)	(0.0910, 0.2042, 0.7048)
1	3	1	(0.0529, 0.1453, 0.8018)	(0.2500, 0.1667, 0.5833)	(0.1071, 0.1512, 0.7417)
1	3	2	(0.4004, 0.2945, 0.3050)	(0.1250, 0.5000, 0.3750)	(0.4443, 0.3100, 0.2457)
2	1	1	(0.0522, 0.1494, 0.7984)	(0, 0.2500, 0.7500)	(0.0333, 0.1451, 0.8216)
2	1	2	(0.7551, 0.2110, 0.0340)	(0.7500, 0, 0.2500)	(0.7007, 0.2177, 0.0816)
2	2	1	(0.8070, 0.1814, 0.0116)	(0.8000, 0.2000, 0)	(0.7805, 0.1718, 0.0477)
2	2	2	(0.0079, 0.0880, 0.9042)	(0, 0.2500, 0.7500)	(0.0258, 0.1497, 0.8245)
2	3	1	(0.0554, 0.1121, 0.8325)	(0, 0, 1.0)	(0.0362, 0.1527, 0.8111)
2	3	2	(0.2352, 0.3737, 0.3912)	(0.5000, 0.5000, 0)	(0.2587, 0.3361, 0.4052)
3	1	1	(0.3241, 0.5621, 0.1138)	(0.3333, 0.3333, 0.3333)	(0.3030, 0.5827, 0.1144)
3	1	2	(0.5580, 0.2853, 0.1568)	(0.7333, 0.1333, 0.1333)	(0.5797, 0.2805, 0.13981)
3	2	1	(0.5889, 0.2480, 0.1631)	(0.4444, 0.3333, 0.2222)	(0.5311, 0.2518, 0.2171)
3	2	2	(0.4282, 0.3757, 0.1961)	(1.000, 0, 0)	(0.5594, 0.2651, 0.1755)
3	3	1	(0.2715, 0.3737, 0.3548)	(0.2222, 0.3337, 0.4444)	(0.2533, 0.3028, 0.4439)
3	3	2	(0.2096, 0.2227, 0.5677)	(0, 0, 1.000)	(0.1721, 0.2751, 0.5527)