A Bayesian framework for adaptive selection, calibration, and validation of coarse-grained models of atomistic systems

DOI10.1016/j.jcp.2015.03.071zbMath1349.62078OpenAlexW2041229203MaRDI QIDQ350057

Danial Faghihi, J. Tinsley Oden, Kathryn Farrell

Publication date: 5 December 2016

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2015.03.071

zbMATH Keywords

Bayesian inferencemodel validationcoarse graining modelsmodel plausibilityoutput sensitivities

Mathematics Subject Classification ID

Bayesian inference (62F15)

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Uses Software

• CHARMM
• QUESO

Cites Work

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• On the Relative Importance of Input Factors in Mathematical Models
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• PRIOR AND POSTERIOR ROBUST STOCHASTIC PREDICTIONS FOR DYNAMICAL SYSTEMS USING PROBABILITY LOGIC
• Prior Probabilities

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