This project is contributed to by the following subprojects :
Markov Chain Monte-Carlo methods are used to calculate probability density functions for parameters in dynamic systems models. By virtue of computation of the true posterior density, these methods allow accurate quantification of estimation error, even for short data lengths.
This toolbox is a MATLAB-based software package for the estimation of dynamic systems.
A wide range of standard estimation approaches are supported. These include the use of non-parametric, subspace-based and prediction-error algorithms coupled (in the latter case) with either MIMO state space or MISO polynomial model structures.
Additionally, some new approaches are included. These include the support for bilinear and other Hammerstein-Wiener non-linear structures, and the use of the expectation-maximisation (EM) algorithm for time and frequency domain estimation of state space structures.
This project develops quantifications for the frequency domain variance of prediction error system estimates. A key theme is to derive new approximations offering improved accuracy via the principles of reproducing kernel principles and orthonormal parametrizations.
Details of our attempt at the Wiener-Hammerstein Benchmark problem