Near Real-Time Quantification of Stochastic Model Parameters

Mathematical models of physical and biological systems contain parameters that need to be estimated from measured data. Models with parameters distributed probabilistically require the estimates of a probability measure over the set of admissible parameters. We propose to use frequentist-based approaches for non-parametrically estimating probability measures that describe the distribution of parameters across all members of a given population in the case where only aggregate longitudinal data are available. We will develop mathematical models for specific biological and physical systems of current interest to U.S. Army Natick Soldier Research, Development and Engineering Center (NSRDEC), estimate model parameters, and quantify and propagate uncertainty in these systems. Software implementing the algorithms will be developed for use in real-time estimation.