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ICASSP
2011
IEEE
12 years 8 months ago
MAP-based estimation of the parameters of non-stationary Gaussian processes from noisy observations
The paper proposes a modification of the standard maximum a posteriori (MAP) method for the estimation of the parameters of a Gaussian process for cases where the process is supe...
Alexander Krueger, Reinhold Haeb-Umbach
ICASSP
2011
IEEE
12 years 8 months ago
Evolutive method based on a generalized eigenvalue decomposition to estimate time varying autoregressive parameters from noisy o
A great deal of interest has been paid to the estimation of time-varying autoregressive (TVAR) parameters. However, when the observations are disturbed by an additive white measur...
Hiroshi Ijima, Julien Petitjean, Eric Grivel
SIVP
2010
184views more  SIVP 2010»
13 years 3 months ago
Multichannel AR parameter estimation from noisy observations as an errors-in-variables issue
In various applications from radar processing to mobile communication systems based on CDMA for instance, M-AR multichannel processes are often considered and may be combined with...
Julien Petitjean, Eric Grivel, William Bobillet, P...
ICASSP
2008
IEEE
13 years 11 months ago
Stability analysis of the consensus-based distributed LMS algorithm
We deal with consensus-based online estimation and tracking of (non-) stationary signals using ad hoc wireless sensor networks (WSNs). A distributed (D-) least-mean square (LMS) l...
Ioannis D. Schizas, Gonzalo Mateos, Georgios B. Gi...
NIPS
2007
13 years 6 months ago
Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes
Neural spike trains present challenges to analytical efforts due to their noisy, spiking nature. Many studies of neuroscientific and neural prosthetic importance rely on a smooth...
John P. Cunningham, Byron M. Yu, Krishna V. Shenoy...