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Parameter Estimation with Trees (9.0)

Parameter Estimation with Trees (9.0)

Overview & Context

  • In BayesiaLab, a Conditional Probability Distribution (CPD) can be compactly represented with a Conditional Probability Tree (CPTr), which takes advantage of Contextual Independencies.
  • A Contextual Independence exists if a particular state of a parent node makes the other co-parent(s), i.e., the spouse(s), independent of the child node.

New Feature: EQ, TabooEQ, and SopLEQ

  • As of version 9.0, all unsupervised structural learning algorithms are compatible with estimating probabilities with Conditional Probability Trees.

Usage

  • To use Conditional Probability Trees for estimation, select Edit > Parameter Estimation with Trees  from the Main Menu.

  • Alternatively, you can select Parameter Estimation with Trees  from the Graph Contextual Menu.

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