Name
Abstract | ||
|---|---|---|
This paper investigates the benefits of intentionally adding noise to a Bayesian estimator, which comprises a linear combination of a number of individual Bayesian estimators that are perturbed by mutually independent noise sources and multiplied by a set of adjustable weighting coefficients. We prove that the Bayes risk for the mean square error (MSE) criterion is minimized when the same optimum ... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TSP.2019.2931203 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
Bayes methods,Noise level,Probability density function,Reactive power,Estimation,Stochastic resonance,Noise measurement | Mathematical optimization,Weighting,Mean squared error,Algorithm,Estimation theory,Nonlinear filter,Independence (probability theory),Mathematics,Estimator,Bayesian probability,Bayes' theorem | Journal |
Volume | Issue | ISSN |
67 | 17 | 1053-587X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabing Duan | 1 | 22 | 5.05 |
| Yan Pan | 2 | 179 | 19.23 |
| François Chapeau-Blondeau | 3 | 202 | 42.14 |
| D Abbott | 4 | 109 | 21.31 |