Accession Number:

ADA556799

Title:

Nonlinear Stochastic Markov Processes and Modeling Uncertainty in Populations

Descriptive Note:

Corporate Author:

NORTH CAROLINA STATE UNIV AT RALEIGH CENTER FOR RESEARCH IN SCIENTIFIC COMPUTATION

Personal Author(s):

• Banks, H. T.
• Hu, Shuhua

Report Date:

2011-07-06

Pagination or Media Count:

32.0

Abstract:

We consider an alternative approach to the use of nonlinear stochastic Markov processes which have a Fokker-Planck or Forward Kolmogorov representation for density in modeling uncertainty in populations. These alternate formulations, which involve imposing probabilis- tic structures on a family of deterministic dynamical systems, are shown to yield pointwise equivalent population densities. Moreover, these alternate formulations lead to fast efficient calculations in inverse problems as well as in forward simulations. Here we derive a class of stochastic formulations for which such an alternate representation is readily found.

Descriptors:

• *MARKOV PROCESSES
• EFFICIENCY
• FOKKER PLANCK EQUATIONS
• FORMULATIONS
• INVERSE PROBLEMS
• NONLINEAR SYSTEMS
• POPULATION
• STOCHASTIC PROCESSES
• UNCERTAINTY

Subject Categories:

• Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE