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BayesiaLabKey ConceptsMutual Information

Mutual Information

The Mutual Information I(X,Y)I(X, Y) measures the amount of information gained on variable XX (the reduction in the Expected Log-Loss) by observing variable YY:

I(X,Y)=H(X)H(XY)\displaystyle I(X,Y) = H(X) - H(X \mid Y)

The Venn Diagram below illustrates this concept:

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The Conditional Entropy H(XY)H(X \mid Y) measures, in bits, the Expected Log-Loss associated with variable XX once we have information on variable YY:

H(XY)=yYp(y)xXp(xy)log2(p(xy))\displaystyle H(X \mid Y) = - \sum\limits_{y \in Y} {p(y)\sum\limits_{x \in X} {p(x \mid y){{\log }_2}} } \left( {p(x \mid y)} \right)

Hence, the Conditional Entropy is a key element in defining the Mutual Information between XX and YY.

Note that

I(X,Y)=H(X)H(XY)\displaystyle I(X,Y) = H(X) - H(X \mid Y)

is equivalent to:

I(X,Y)=xXyYp(x,y)log2p(x,y)p(x)p(y)\displaystyle I(X,Y) = \sum\limits_{x \in X} {\sum\limits_{y \in Y} {p(x,y){{\log }_2}} } {{p(x,y)} \over {p(x)p(y)}}

and furthermore equivalent to:

I(X,Y)=yYp(y)xXp(xy)log2p(xy)p(x)\displaystyle I(X,Y) = \sum\limits_{y \in Y} {p(y)\sum\limits_{x \in X} {p(x \mid y){{\log }_2}} } {{p(x \mid y)} \over {p(x)}}

This allows computing the Mutual Information between any two variables.

Usage

For a given network, BayesiaLab can report the Mutual Information in several contexts:

  • Menus > Analysis > Report > Target > Relationship with Target Node.
  • Note that this table shows the Mutual Information of each node, e.g., XRay, Dyspnea, etc., only with regard to the Target Node, Cancer.
Target Analysis report showing Mutual Information for each node
  • Menus > Analysis > Report > Relationship Analysis:
Relationship Analysis report showing Mutual Information
  • The Mutual Information can also be shown by selecting Menus > Analysis > Visual > Overall > Arc > Mutual Information and then clicking the Show Arc Comments icon or selecting Menu > View > Show Arc Comments.
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  • Note that the corresponding options under Preferences > Analysis > Visual Analysis > Arc's Mutual Information Analysis have to be selected first:
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  • In Preferences, Child refers to the Relative Mutual Information from the Parent onto the Child node, i.e., in the direction of the arc.
  • Conversely, Parent refers to the Relative Mutual Information from the Child onto the Parent node, i.e., in the opposite direction of the arc.