Bayes Risk Reduction of Estimators Using Artificial Observation Noise

DOI10.1109/TSP.2015.2457394zbMath1394.94611MaRDI QIDQ4580868

Stefan Uhlich

Publication date: 22 August 2018

Published in: IEEE Transactions on Signal Processing (Search for Journal in Brave)

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (3)

Benefits of noise in M-estimators: optimal noise level and probability density ⋮ Enhancing threshold neural network via suprathreshold stochastic resonance for pattern classification ⋮ Training threshold neural networks by extreme learning machine and adaptive stochastic resonance

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