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Correction: A guide to bayesian networks software for structure and parameter learning, with a focus on causal discovery tools.

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A guide to bayesian networks software for structure and parameter learning, with a focus on causal discovery tools.

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Summary

This paper reviews software for Bayesian Networks (BNs), essential for AI to understand cause-effect mechanisms. It guides beginners through structural and parameter learning tools, aiding easier entry into this complex field.

Keywords:
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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Causal Inference

Background:

  • Bayesian Networks (BNs) are crucial for AI to model cause-effect relationships.
  • BNs involve structural and parameter learning, with diverse algorithms and tools available.
  • The variety of software presents challenges for beginners entering the field.

Purpose of the Study:

  • To review and recommend software for Bayesian Networks (BNs) structural and parameter learning.
  • To focus on causal discovery tools relevant to AI.
  • To enhance accessibility for beginners by providing clear guidance and a summary table.

Main Methods:

  • Systematic review of existing software and tools for BNs.
  • Focus on causal discovery algorithms and their implementations.
  • Development of a comparative overview table of software features.

Main Results:

  • Identification of key software packages for BNs structural and parameter learning.
  • Subjective recommendations tailored for beginners.
  • A comprehensive table summarizing software features and capabilities.

Conclusions:

  • Beginners can navigate BNs software selection more effectively with this review.
  • Improved accessibility to BNs tools facilitates AI's representation of causal mechanisms.
  • The reviewed tools support advancements in causal discovery and AI.