Name
Abstract | ||
|---|---|---|
Modern signal processing (SP) methods rely very heavily on probability and statistics to solve challenging SP problems. SP methods are now expected to deal with ever more complex models, requiring ever more sophisticated computational inference techniques. This has driven the development of statistical SP methods based on stochastic simulation and optimization. Stochastic simulation and optimizati... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/JSTSP.2015.2496908 | IEEE Journal of Selected Topics in Signal Processing |
Keywords | Field | DocType |
Signal processing algorithms,Approximation algorithms,Stochastic processes,Computational modeling,Optimization,Monte Carlo methods,Proposals | Stochastic simulation,Mathematical optimization,Probabilistic-based design optimization,Stochastic optimization,Bayesian inference,Markov chain Monte Carlo,Computer science,Statistical model,Statistical inference,Stochastic programming | Journal |
Volume | Issue | ISSN |
10 | 2 | 1932-4553 |
Citations | PageRank | References |
23 | 0.84 | 69 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcelo Pereyra | 1 | 142 | 16.00 |
| Philip Schniter | 2 | 1620 | 93.74 |
| Emilie Chouzenoux | 3 | 202 | 26.37 |
| Jean-Christophe Pesquet | 4 | 83 | 4.45 |
| Jean-Yves Tourneret | 5 | 1154 | 104.46 |
| Iii Alfred O. Hero | 6 | 1713 | 197.61 |
| Stephen McLaughlin | 7 | 168 | 16.62 |