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PyOED V 2.0.0 - Home PyOED V 2.0.0 - Home
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  • PyOED Source Code Repository
  • PyOED Paper
  • Install
  • Getting Started
  • Examples
  • User Guide
  • Contributors
  • News & More
  • License
  • PyOED Paper
  • PyOED Source Code Repository
  • PyOED Source Code Repository
  • PyOED Paper

Section Navigation

  • Configs
  • Models
  • Data Assimilation (Inverse Problems)
  • Optimal Experimental Design
    • OED utility functions (optimality criteria)
      • Alphabetic Criteria
      • Information-theoretic Criteria
        • EIG Criteria
        • EK-Divergence Criteria
    • OED for sensor placement
    • Robust OED for sensor placement
    • Common (general-purpose OED) algorithms
    • OED core: base classes and functionality
  • Optimization
  • Statistics
  • Machine Learning
  • Utility
  • Extend (Contribute to) PyOED
  • Collaborations and Feature Requests
  • User Guide
  • Optimal Experimental Design Routines
  • OED Utility Functions (Optimality Criteria)
  • Information-theoritic OED Criteria

Information-theoritic OED Criteria#

Information-theoretic OED criteria are based on summaries of the information content or expected information gain. The most commonly used are the expected information gain (EIG Criterion), and the KL-divergence Criteria between prior and the posterior in Bayesian OED.

  • EIG Criterion

  • KL-divergence Criteria

EIG Criterion#

  • EIG Criteria
    • EvaluationMethod
    • BayesianInversionEIGConfigs
    • BayesianInversionEIG

KL-divergence Criteria#

  • EK-Divergence Criteria
    • EvaluationMethod
    • BayesianInversionKLDivergenceGaussianConfigs
    • BayesianInversionKLDivergenceGaussian

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D Optimality (Alphabetic) Criteria

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Expected Information Gain (EIG) Optimality Criteria

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  • EIG Criterion
  • KL-divergence Criteria

Last updated on Mar 06, 2025.

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