Hierarchical Bayesian approaches for robust inference in ARX models
Reversible-Jump Markov Chain Monte Carlo sampler for inference in ARX models with Student-t innovations
This code was downloaded from < https://github.com/compops/rjmcmc-sysid2012 > and contains the code used to produce the results in
J. Dahlin, F. Lindsten, T. B. Schön and A. Wills, Hierarchical Bayesian ARX models for robust inference. In the Proceedings of the 16th IFAC Symposium on System Identification, Brussels, Belgium, July 2012.
which is available from < http://www.johandahlin.com/files/publications/Dahlin2012-RJMCMC.pdf >.
RUNME_simulationstudy
The code reproduces the comparision presented in Section 5.1. Note that the
results from that section is based on 25 000 randomly generated systems and
the provided code only simulates 100 systems.
RUNME_EEG
The code reproduces a similar output as presented in Fig 3 of Section 5.3.
Using real EEG data, kindly provided by Eline Borch Petersen and Thomas Lunner
at Eriksholm Research Centre, Oticon A/S, Denmark.
Helpfiles and subroutines are found in the folder “helpers”: