Some other features in Bayesian inference

Lazy Propagation uses a secondary structure called the “Junction Tree” to perform the inference.

import pyagrum as gum
import pyagrum.lib.notebook as gnb

bn = gum.loadBN("res/alarm.bgum")
gnb.showJunctionTreeMap(bn);

But this junction tree can be transformed to build different probabilistic queries.

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

Evidence impact

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

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

		B
A	H	0	1
0	0	0.8051	0.1949
	1	0.7808	0.2192
1	0	0.4131	0.5869
	1	0.3777	0.6223

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
A	B	D	0	1
0	0	0	0.1079	0.8921
		1	0.0780	0.9220
	1	0	0.1365	0.8635
		1	0.0517	0.9483
1	0	0	0.4581	0.5419
		1	0.3715	0.6285
	1	0	0.5249	0.4751
		1	0.2759	0.7241

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

			E
C	A	D	0	1
0	0	0	0.3118	0.6882
		1	0.0010	0.9990
	1	0	0.7599	0.2401
		1	0.0069	0.9931
1	0	0	0.0605	0.9395
		1	0.1935	0.8065
	1	0	0.3102	0.6898
		1	0.6263	0.3737

Evidence Joint Impact

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

			A
E	B	F	0	1
0	0	0	0.0399	0.2209
		1	0.1544	0.5848
	1	0	0.0087	0.1590
		1	0.0308	0.8015
1	0	0	0.1288	0.1021
		1	0.4987	0.2704
	1	0	0.0491	0.1287
		1	0.1739	0.6483