Near Real-Time Quantification of Stochastic Model Parameters
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 (QCM)developed by the U.S. Army Natick Soldier Research Development and Engineering Center(NSRDEC) (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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 28, 2016
- Accession Number
- AD1100945
Entities
People
- H. Thomas Banks
- Jared Catenacci
- Panagiotis Tsilifis
- Paul K. Newton
- Roger Ghanem
- Thomas E Wood
- William J. Browning