Target Dynamic Profile (9.0)

Context

Analysis | Target Optimization | Dynamic Profile

This feature is a greedy search algorithm that can be used to find the sequence of observations (Hard Evidence and/or Numerical Evidence) that optimizes:

the probability of the target state,
the mean value of the target node,
the probability difference between two states.

History

Target Dynamic Profile has been updated in versions 5.0.7, 5.2, and 5.3.

Update and New Features: Target State Optimization Report

As of version 9.0, the report describing the sequence of observations for optimizing a state of the Target Node comes with several new and renamed metrics:

Optimal State (for optimization with Hard Evidence) and Posterior Value/Mean (for optimization with Numerical Evidence) have been renamed Evidence,
Probability has been renamed Posterior Probability P(s|E), where s stands for the state to be optimized,
Joint Probability has been renamed Marginal LikelihoodP(E)**, where E represents the current set of evidence,
Likelihood P(E|s),
Bayes Factor BF(s,E): P(s|E)/P(s),
Generalized Bayes Factor GBF(s, E).

Now, it is also possible to generate Histograms via the Report Button , with the Posterior ProbabilityP(s|E)** and the LikelihoodP(E|s).**

Example

Let’s utilize the network we have used for describing Cluster Interpretation with Most Relevant Explanations (MRE).

We use the Target Dynamic Profile to search for the best 3-piece of Hard Evidence that characterizes the men associated with C3.

Updated Feature: Target Mean Optimization Report

The report describing the sequence of observations for optimizing the Mean Value of the Target Node has also been slightly modified:

Optimal State (for optimization with Hard Evidence) and Posterior Value/Mean (for optimization with Numerical Evidence) have been renamed Evidence,
95% Credible Interval has been renamed Confidence Interval, and the interval is represented instead of just the delta from the mean,
Joint Probability has been renamed Marginal LikelihoodP(E)**, where E represents the current set of evidence.

As of version 9.0, the Confidence Level can now be modified via the Preferences

Example

Let’s use the previous network to illustrate this feature. Since we checked the option “Create Cluster with Ordered Numerical States” during Clustering, the numerical value of each cluster is the weighted average of the posterior mean values of the measurements.

We are looking for the best sequence of Numerical Evidence that maximizes the Mean Value of the Target Node.