Figure 1 shows the correlation analysis dialog as it appears after
being activated. In dialog the correlation between a pair of discrete
chance node can be analysed using a number of different measures.
![]() |
Figure 1: The Correlation Analysis Dialog. |
The dialog supports three different methods for analysing the correlation between a pair of nodes X and Y: the conditional probabability distribution, the joint probability distribution, and the Pearson test.
In the following sections the three methods for analysing the correlation between a pair of discrete chance variables are described.
Figure 2 shows the conditional probability distribution of
X given Y computed based on the entered and
propagated evidence.
![]() |
Figure 2: The conditional probability distribution of X given Y. |
Notice that X and Y can in principle be any pair of discrete chance nodes in the model.
The computation of the conditional probability distribution may in principle fail with an out-of-memory error. This will happen if the tables in the underlying junction tree become too large during the process of computing the conditional.
Figure 3 shows the joint probability distribution of X and
Y computed based on the entered and propagated evidence.
![]() |
Figure 3: The joint probability distribution over X and Y. |
The computation of the joint probability distribution may in principle fail with an out-of-memory error. This will happen if the tables in the underlying junction tree become too large during the process of computing the joint.
Figure 4 shows the
joint counts table X and Y computed based on the entered
and propagated evidence.
![]() |
Figure 4: A table of counts over X and Y. |