Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICASSP.2010.5495806 | Acoustics Speech and Signal Processing |
Keywords | Field | DocType |
Bayes methods,Markov processes,Monte Carlo methods,signal classification,signal detection,signal sampling,Bayesian detection,Markov chain Monte Carlo method,deterministic local constraint,discrete random parameter classification,minimum distance,nonuniform spaced binary pulses,partial collapsed Gibbs sampler,probabilistic structure,Markov chain Monte Carlo method,deterministic constraints,partially collapsed Gibbs sampler,pulse detection | Convergence (routing),Mathematical optimization,Monte Carlo method,Markov process,Markov chain Monte Carlo,Computer science,Probabilistic logic,Gibbs sampling,Binary number,Bayesian probability | Conference |
ISSN | ISBN | Citations |
1520-6149 E-ISBN : 978-1-4244-4296-6 | 978-1-4244-4296-6 | 5 |
PageRank | References | Authors |
0.56 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Georg Kail | 1 | 5 | 0.89 |
| Jean-Yves Tourneret | 2 | 835 | 64.32 |
| Franz Hlawatsch | 3 | 9 | 1.06 |
| Nicolas Dobigeon | 4 | 2070 | 108.02 |