Workflow 2

Workflow Instructions

  • In Workflow 1, we exported a Structural Prior Dictionary, including the Causal Structural Priors, and then imported this dictionary as an Arc Dictionary to create a causal network with these priors.
  • In this Workflow 2, we will immediately utilize the Causal Structural Priors to machine-learning a new network without the export/import step.
  • So, our starting point is the machine-learned network, for which Hellixia has already obtained the Causal Structural Priors. The Structural Prior icon indicates that Structural Priors are associated with the network.
  • However, these new Causal Structural Priors have not been used for updating the arc directions in the network.
  • Select Menu > Learning > Unsupervised Structural Learning > Taboo.

Like Arc Constraints, Structural Priors, Temporal Indices, and Filtered States, Causal Structural Priors impose constraints on learning. As a result, EQ-based algorithms are not available under those conditions.

  • This newly learned network now reflects the causal order obtained from ChatGPT.
  • With the final arc directions in place, we should arrange the nodes into a more intuitive layout, i.e., positioning parent nodes above child nodes.
  • Select Menu > View > Layout > Genetic Grid Layout > Top-Down Repartition.
  • Note that the algorithm keeps searching for a better layout until you stop the process by clicking the red buttonto the left of the Progress Bar.

Workflow Illustration

For North America

Bayesia USA

4235 Hillsboro Pike
Suite 300-688
Nashville, TN 37215, USA

+1 888-386-8383

Head Office

Bayesia S.A.S.

Parc Ceres, Batiment N 21
rue Ferdinand Buisson
53810 Change, France

For Asia/Pacific

Bayesia Singapore

1 Fusionopolis Place
#03-20 Galaxis
Singapore 138522

Copyright © 2024 Bayesia S.A.S., Bayesia USA, LLC, and Bayesia Singapore Pte. Ltd. All Rights Reserved.