Skip to main content
U.S. Department of Energy
Office of Scientific and Technical Information

Adaptive selection and validation of models of complex systems in the presence of uncertainty

Journal Article · · Research in the Mathematical Sciences
 [1];  [2]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Univ. of Texas, Austin, TX (United States)

This study describes versions of OPAL, the Occam-Plausibility Algorithm in which the use of Bayesian model plausibilities is replaced with information theoretic methods, such as the Akaike Information Criterion and the Bayes Information Criterion. Applications to complex systems of coarse-grained molecular models approximating atomistic models of polyethylene materials are described. All of these model selection methods take into account uncertainties in the model, the observational data, the model parameters, and the predicted quantities of interest. A comparison of the models chosen by Bayesian model selection criteria and those chosen by the information-theoretic criteria is given.

Research Organization:
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
Grant/Contract Number:
AC04-94AL85000
OSTI ID:
1356828
Report Number(s):
SAND--2017-2722J; PII: 104
Journal Information:
Research in the Mathematical Sciences, Journal Name: Research in the Mathematical Sciences Journal Issue: 1 Vol. 4; ISSN 2197-9847
Publisher:
SpringerOpenCopyright Statement
Country of Publication:
United States
Language:
English

References (17)

Understanding predictive information criteria for Bayesian models journal August 2013
Calibration and validation of coarse-grained models of atomic systems: application to semiconductor manufacturing journal May 2014
Regression and time series model selection in small samples journal January 1989
Information Criteria and Statistical Modeling book October 2007
A Bayesian framework for adaptive selection, calibration, and validation of coarse-grained models of atomistic systems journal August 2015
A comparison of the information and posterior probability criteria for model selection journal May 1981
Probability Theory book January 2003
The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin journal March 1988
Science and Statistics journal December 1976
A new look at the statistical model identification journal December 1974
Estimating the Dimension of a Model journal March 1978
Model Selection and Model Averaging in Phylogenetics: Advantages of Akaike Information Criterion and Bayesian Approaches Over Likelihood Ratio Tests journal October 2004
Model Selection Using Response Measurements: Bayesian Probabilistic Approach journal February 2004
Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids journal January 1996
The Bernstein-Von-Mises theorem under misspecification journal January 2012
From Laplace to Supernova SN 1987A: Bayesian Inference in Astrophysics book January 1990
Bayesian calibration, validation, and uncertainty quantification of diffuse interface models of tumor growth journal October 2012

Cited By (3)

Bayesian calibration of force-fields from experimental data: TIP4P water journal October 2018
Bayesian Calibration of Force-fields from Experimental Data: TIP4P Water text January 2018
Embedded discrepancy operators in reduced models of interacting species preprint January 2019

Similar Records

A Bayesian framework for adaptive selection, calibration, and validation of coarse-grained models of atomistic systems
Journal Article · 2015 · Journal of Computational Physics · OSTI ID:22465641

On Model Selection Criteria in Multimodel Analysis
Journal Article · 2008 · Water Resources Research, 44(3):Art. no. W03428 · OSTI ID:927977

Bayesian modeling of source confusion in LISA data
Journal Article · 2005 · Physical Review. D, Particles Fields · OSTI ID:20711097

Related Subjects