Accession Number:

AD1100945

Title:

Near Real-Time Quantification of Stochastic Model Parameters

Descriptive Note:

STTR Report,01 Oct 2014,30 Sep 2016

Corporate Author:

Applied Mathematics, Inc. Gales Ferry United States

Report Date:

2016-09-28

Pagination or Media Count:

321.0

Abstract:

We quantify the uncertainty in estimated model parameters using both a Bayesian and a Frequentist approach. We then apply these methods to a class of quasi-chemical models QCMdeveloped by the U.S. Army Natick Soldier Research Development and Engineering CenterNSRDEC References a and b. The QCM models, developed to model bacteria growth on food under various environmental conditions, are capable of capturing the effects of microbial lag, inactivation and tailing. Bayesian and Frequentist approaches for solving the inverse problem are presented in this report. Solutions using the two approaches for the datasets from Reference b are compared. Uncertainty Quantification UQ methods for Forward Uncertainty Propagation are also applied using the datasets.

Subject Categories:

  • Theoretical Mathematics
  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE