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

Table 10

Parameters learned by 500 samples.

	3000-MLE	500-MLE	500-MCE
T	Ai(1, 2)	Ai(1, 2)	Ai(1, 2)
T1 (strong)	(0.9024, 0.0976)	(0.9286, 0.0714)	(0.9213, 0.0787)
T2 (middle)	(0.5520, 0.4480)	(0, 5573, 0.4427)	(0.5625, 0.4375)
T3 (middle)	(0.2604, 0.7396)	(0.2704, 0.7296)	(0.2692, 0.7308)