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.dsl")
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]

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