Talks and Poster Presentations (with Proceedings-Entry):
G. Kail, J.-Y. Tourneret, F. Hlawatsch, N. Dobigeon:
"A Partially Collapsed Gibbs Sampler for Parameters with Local Constraints";
Poster: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2010),
Dallas (TX), USA;
- 03-19-2010; in: "IEEE International Conference on Acoustics, Speech and Signal Processing, 2010. ICASSP 2010.",
We consider Bayesian detection/classification of discrete random parameters that are strongly dependent locally due to some deterministic local constraint. Based on the recently introduced partially collapsed Gibbs sampler (PCGS) principle, we develop a Markov chain Monte Carlo method that tolerates and even exploits the challenging probabilistic structure imposed by deterministic local constraints. We study the application of our method to the practically relevant case of nonuniformly spaced binary pulses with a known minimum distance. Simulation results demonstrate significant performance gains of our method compared to a recently proposed PCGS that is not specifically designed for the local constraint.
Markov chain Monte Carlo method, partially collapsed Gibbs sampler, pulse detection, deterministic constraints
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.