Table 3.

Using Partial-MPF to reconstruct landscapes in the presence of partially-observed data. While the “naive” strategy actually decreases the quality of the fit, Partial-MPF enables efficient use of partial observations to improve knowledge of the landscape.

$\mathit{\beta }$ Range	128 Full	128 Full + 128 Partial		256 Full
		Partial-MPF	Naive
0.01–0.125 (dispersed)	0.23	0.17	0.23	0.15
0.125–0.25 (ordered)	0.56	0.41	0.56	0.34
0.25–0.5 (near critical)	0.63	0.44	0.81	0.40
0.5–1.0 (critical)	0.54	0.41	1.06	0.38