Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models

Chris Oates, Steven Niederer, Angela Lee, François-Xavier Briol, Mark Girolami

Nov-21-2025, 11:56:42 GMT–Neural Information Processing Systems

The computational model can be assessed through comparison of these predictions to test data generated from an experiment.

artificial intelligence, machine learning, numerical error, (16 more...)

Neural Information Processing Systems

Nov-21-2025, 11:56:42 GMT

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Country:
- Asia > Japan
  - Honshū > Kantō > Kanagawa Prefecture (0.04)
- North America > United States
  - California > Los Angeles County > Long Beach (0.04)

Genre:
- Research Report (1.00)

Industry:
- Health & Medicine > Therapeutic Area > Cardiology/Vascular Diseases (0.68)

Technology:
- Information Technology > Artificial Intelligence
  - Machine Learning > Learning Graphical Models
    - Directed Networks > Bayesian Learning (0.93)
  - Representation & Reasoning > Uncertainty
    - Bayesian Inference (0.93)

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Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models
Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models

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