HR: 0800h
AN: IN41A-0311    [Abstracts]
TI: Structure Learning in Stochastic Non-linear Dynamical Systems
AU: * Morris, R D
EM: rdm@email.arc.nasa.gov
AF: USRA-RIACS, NASA Ames Resarch Center, MS 269-2, Moffett Field, CA 94035 United States
AU: Smelyanskiy, V N
EM: vadim@email.arc.nasa.gov
AF: NASA, NASA Ames Research Center, Moffett Field, ca 94035 United States
AU: Luchinsky, D G
EM: luchinsk@email.arc.nasa.gov
AF: MCT, NASA Ames Research Center, Moffett Field, ca 94035 United States
AB: A great many systems can be modeled in the non-linear dynamical systems framework, as ẋ = f(x) + ξ(t), where f(x) is the potential function for the system, and ξ(t) is the driving noise. Modeling the potential using a set of basis functions, we derive the posterior for the basis coefficients. A more challenging problem is to determine the set of basis functions that are required to model a particular system. We show that using the Bayesian Information Criteria (BIC) to rank models, and the beam search technique, that we can accurately determine the structure of simple non-linear dynamical system models, and the structure of the coupling between non-linear dynamical systems where the individual systems are known. This last case has important ecological applications, for example in predator-prey systems, where the very structure of the coupling between predator-prey pairs can have great ecological significance.
DE: 0430 Computational methods and data processing
DE: 0439 Ecosystems, structure and dynamics (4815)
DE: 0466 Modeling
DE: 0491 Food webs and trophodynamics (4817)
DE: 0520 Data analysis: algorithms and implementation
SC: Earth and Space Science Informatics [IN]
MN: Fall Meeting 2005