Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1898339 | Physica D: Nonlinear Phenomena | 2006 | 7 Pages |
Abstract
We demonstrate how unknown process rates within a stochastic modelling framework based on Markov processes can be approximated from time series data using polynomial basis functions. The problem of model selection is considered by adapting basis function selection methods and the minimum description length information criteria which have previously been developed for nonlinear autoregressive models of time series under Gaussian noise assumptions. We investigate the effectiveness of the methods with application to stochastic biological population models.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
David M. Walker, Glenn Marion,