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

Table 9

Parameters learned by 500 samples.

	3000-MLE	500-MLE	500-MCE
T	Ac(1, 2, 3)	Ac(1, 2, 3)	Ac(1, 2, 3)
T1 (strong)	(0.8102, 0.1399, 0.0499)	(0.8393, 0.1250, 0.0357)	(0.8172, 0.1381, 0.0447)
T2 (middle)	(0.3027, 0.4038, 0.2935)	(0.2552, 0.3906, 0.3542)	(0.2498, 0.4132, 0.3370)
T3 (weak)	(0.2482,0.3411,0.4107)	(0.2449,0.3061,0.4490)	(0.2409,0.3081,0.4510)