Systematic variational method for statistical nonlinear state and parameter estimation

TitleSystematic variational method for statistical nonlinear state and parameter estimation
Publication TypeJournal Article
Year of Publication2015
AuthorsYe J.X, Rey D., Kadakia N., Eldridge M., Morone U.I, Rozdeba P., Abarbanel H.DI, Quinn J.C
JournalPhysical Review E
Volume92
Date Published2015/11
Type of ArticleArticle
ISBN Number1539-3755
Accession NumberWOS:000364022300006
Keywordsalgorithm; continuous-time; Data assimilation; optimization
Abstract

In statistical data assimilation one evaluates the conditional expected values, conditioned on measurements, of interesting quantities on the path of a model through observation and prediction windows. This often requires working with very high dimensional integrals in the discrete time descriptions of the observations and model dynamics, which become functional integrals in the continuous-time limit. Two familiar methods for performing these integrals include (1) Monte Carlo calculations and (2) variational approximations using the method of Laplace plus perturbative corrections to the dominant contributions. We attend here to aspects of the Laplace approximation and develop an annealing method for locating the variational path satisfying the Euler-Lagrange equations that comprises the major contribution to the integrals. This begins with the identification of the minimum action path starting with a situation where the model dynamics is totally unresolved in state space, and the consistent minimum of the variational problem is known. We then proceed to slowly increase the model resolution, seeking to remain in the basin of the minimum action path, until a path that gives the dominant contribution to the integral is identified. After a discussion of some general issues, we give examples of the assimilation process for some simple, instructive models from the geophysical literature. Then we explore a slightly richer model of the same type with two distinct time scales. This is followed by a model characterizing the biophysics of individual neurons.

DOI10.1103/PhysRevE.92.052901
Short TitlePhys. Rev. E
Student Publication: 
No
Research Topics: 
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