This pyAgrum's notebook is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

import pyAgrum as gum
import pyAgrum.lib.notebook as gnb

bn=gum.fastBN("A->B->C->D;A->E->D;F->B;C->H")
ie=gum.LazyPropagation(bn)
bn              

Evidence Impact allows the user to analyze the effect of any variables on any other variables

ie.evidenceImpact("B",["A","H"])

		B
H	A	0	1
0	0	0.8162	0.1838
1		0.7793	0.2207
0	1	0.9022	0.0978
1		0.8800	0.1200

Evidence impact is able to find the minimum set of variables which effectively conditions the analyzed variable

ie.evidenceImpact("E",["A","F","B","D"]) # {A,D,B} d-separates E and F

			E
D	B	A	0	1
0	0	0	0.3734	0.6266
1			0.2159	0.7841
0	1		0.4912	0.5088
1			0.1745	0.8255
0	0	1	0.5197	0.4803
1			0.3333	0.6667
0	1		0.6368	0.3632
1			0.2774	0.7226

ie.evidenceImpact("E",["A","B","C","D","F"]) # {A,C,D} d-separates E and {B,F}

			E
D	A	C	0	1
0	0	0	0.9067	0.0933
1			0.1156	0.8844
0	1		0.9464	0.0536
1			0.1919	0.8081
0	0	1	0.1495	0.8505
1			0.4881	0.5119
0	1		0.2420	0.7580
1			0.6339	0.3661

ie.evidenceJointImpact(["A","F"],["B","C","D","E","H"]) # {B,E} d-separates [A,F] and [C,D,H]

			A
F	B	E	0	1
0	0	0	0.0004	0.0576
1			0.0117	0.9303
0	1		0.0120	0.6367
1			0.0128	0.3385
0	0	1	0.0007	0.0570
1			0.0211	0.9212
0	1		0.0214	0.6240
1			0.0228	0.3318