Some other features in Bayesian inference

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

In [1]:

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.

In [2]:

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

Out[2]:

Evidence impact

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

In [3]:

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

Out[3]:

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

In [4]:

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

Out[4]:

In [5]:

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

Out[5]:

In [6]:

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

Out[6]: