Name
Abstract | ||
|---|---|---|
Recently, methods based on Data Augmentation (DA) strategies have shown their efficiency for dealing with high dimensional Gaussian sampling within Gibbs samplers compared to iterative-based sampling (e.g., Perturbation-Optimization). However, they are limited by the feasibility of the direct sampling of the auxiliary variable. This paper reviews DA sampling algorithms for Gaussian sampling and proposes a DA method which is especially useful when direct sampling of the auxiliary variable is not straightforward from a computational viewpoint. Experiments in two vibration analysis applications show the good performance of the proposed algorithm. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.23919/EUSIPCO.2019.8902496 | 2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) |
Keywords | Field | DocType |
Data augmentation, Auxiliary variables, MCMC, Gaussian, Correlation, Bayesian | Markov chain Monte Carlo,Computer science,Algorithm,Auxiliary variables,Gaussian,Direct sampling,Sampling (statistics),Vibration,Gibbs sampling,Bayesian probability | Conference |
ISSN | Citations | PageRank |
2076-1465 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yosra Marnissi | 1 | 0 | 0.34 |
| D. Abboud | 2 | 0 | 0.34 |
| Emilie Chouzenoux | 3 | 202 | 26.37 |
| Jean-Christophe Pesquet | 4 | 18 | 11.52 |
| M. El-Badaoui | 5 | 0 | 0.34 |
| Amel Benazza-Benyahia | 6 | 271 | 32.72 |