Cluster Interpretation: Posterior Distributions

Background & Context

On this page, we present Posterior Distributions for cluster interpretation as an alternative to Most Relevant Explanations for Cluster Interpretation.

To provide further context for Most Relevant Explanations for Cluster Interpretation, we compare several other approaches that can help interpret individual Clusters:

• Setting Evidence for Cluster Interpretation: Posterior Distributions, Relationship with Target Node, Mosaic Analysis, Posterior Mean Analysis, Segment Profile Analysis, Histograms, Tornado Diagrams,
• Optimization for Cluster Interpretation: Dynamic Profile, Target Optimization Tree

More specifically, we compare all these approaches with regard to characterizing the state $\mathrm{Cluster\ 3}$ of the Cluster Node $\mathit{Factor}_0$ in the reference network.

All analyses and instructions on this page refer to this reference network, which you can download here:

MaleClusters.xbl

XBL

Posterior Distributions for Cluster Interpretation

We bring up all of the network’s nodes by selecting Monitor Panel Contextual Menu > Sort > Target State Correlation. In addition to displaying all Monitors, they are also automatically ordered in terms of their importance with the Target State $\mathrm{C3}$.

The obvious starting point of our exploration is to set the evidence on the factor node that corresponds to our interest in $\mathrm{C3}$, i.e., $\mathit{Factor}_0=\mathit{C3}$.

In addition to showing the posterior distributions of the nodes, BayesiaLab can display Absolute Variations: Monitor Contextual Menu > Absolute Variations. This highlights the difference in the distributions of $\mathrm{C3}$ members versus the marginal distributions.