TABLE 3.

Performance summary across 25 simulated data sets Note. Comparison of true positive rate (TPR), false positive rate (FPR), Matthews correlation coefficient (MCC) and area under the ROC curve (AUC) for structure learning, and Frobenius loss (FL) for precision matrix estimation. The standard error of the mean is given in parentheses. The methods compared are the fused and group graphical lasso of Danaher et al. (2014), separate Bayesian graph estimation with mixture priors of Wang (2015), the joint Bayesian estimation with mixture priors of Shaddox et al. (2018), and the proposed linked precision matrix approach

	All Edges						Differential Edges
	TPR	FPR	MCC	AUC	Fr Loss	# edges	TPR	FPR	MCC	AUC
Fused graphical lasso	0.80	0.07	0.48	0.97	0.065	461	0.74	0.14	0.11	0.24
	(0.01)	(0.003)	(0.01)	(0.001)	(0.001)	(15.1)	(0.01)	(0.001)	(0.003)	(0.01)
Group graphical lasso	0.73	0.08	0.40	0.96	0.077	508	0.68	0.14	0.10	0.13
	(0.01)	(0.003)	(0.005)	(0.001)	(0.001)	(16.3)	(0.02)	(0.004)	(0.003)	(0.004)
Separate estimation with	0.17	0.0002	0.40	0.89	0.099	31	0.16	0.01	0.10	0.84
mixture priors	(0.002)	(3.0×10−5)	(0.003)	(0.001)	(0.001)	(0.5)	(0.01)	(2.0×10−4)	(0.01)	(0.01)
Joint estimation with	0.57	0.03	0.47	0.89	0.327	236	0.53	0.06	0.12	0.84
mixture priors	(0.004)	(3.0×10−4)	(0.003)	(0.002)	(0.003)	(1.6)	(0.02)	(0.001)	(0.004)	(0.01)
Linked precision	0.43	0.0002	0.64	0.95	0.057	77	0.22	0.003	0.23	0.87
matrix approach	(0.01)	(2.6×10−5)	(0.004)	(0.001)	(7.4×10−4)	(1.1)	(0.01)	(9.9×10−5)	(0.019)	(0.01)

For MCC, AUC, and FL, the result reflecting the best performance among the methods compared is marked in bold.