Results 1  10
of
8,437
Blind Beamforming for Non Gaussian Signals
 IEE ProceedingsF
, 1993
"... This paper considers an application of blind identification to beamforming. The key point is to use estimates of directional vectors rather than resorting to their hypothesized value. By using estimates of the directional vectors obtained via blind identification i.e. without knowing the arrray mani ..."
Abstract

Cited by 719 (31 self)
 Add to MetaCart
range of parameters, even when the array is perfectly known to the informed beamformer. The key assumption blind identification relies on is the statistical independence of the sources, which we exploit using fourthorder cumulants. A computationally efficient technique is presented for the blind
A gentle tutorial on the EM algorithm and its application to parameter estimation for gaussian mixture and hidden markov models
, 1997
"... We describe the maximumlikelihood parameter estimation problem and how the Expectationform of the EM algorithm as it is often given in the literature. We then develop the EM parameter estimation procedure for two applications: 1) finding the parameters of a mixture of Gaussian densities, and 2) fi ..."
Abstract

Cited by 693 (4 self)
 Add to MetaCart
We describe the maximumlikelihood parameter estimation problem and how the Expectationform of the EM algorithm as it is often given in the literature. We then develop the EM parameter estimation procedure for two applications: 1) finding the parameters of a mixture of Gaussian densities, and 2
Gaussian interference channel capacity to within one bit
 5534–5562, 2008. EURASIP Journal on Advances in Signal Processing
"... Abstract—The capacity of the twouser Gaussian interference channel has been open for 30 years. The understanding on this problem has been limited. The best known achievable region is due to Han and Kobayashi but its characterization is very complicated. It is also not known how tight the existing o ..."
Abstract

Cited by 452 (28 self)
 Add to MetaCart
Abstract—The capacity of the twouser Gaussian interference channel has been open for 30 years. The understanding on this problem has been limited. The best known achievable region is due to Han and Kobayashi but its characterization is very complicated. It is also not known how tight the existing
Probabilityconstrained approach to estimation of random Gaussian parameters
 1st IEEE International Workshop on Computational Advances in MultiSensor Adaptive Processing
, 2005
"... The problem of estimating a random signal vector x observed through a linear transformation H and corrupted by an additive noise is considered. A linear estimator that minimizes the mean squared error (MSE) with a certain selected probability is derived under the assumption that both the additive no ..."
Abstract

Cited by 3 (2 self)
 Add to MetaCart
noise and random signal vectors are zero mean Gaussian with known covariance matrices. Our approach can be viewed as a robust generalization of the Wiener filter. It simplifies to the recently proposed robust minimax estimator in some special cases. 1.
Longitudinal data analysis using generalized linear models”.
 Biometrika,
, 1986
"... SUMMARY This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence. The estimating ..."
Abstract

Cited by 1526 (8 self)
 Add to MetaCart
SUMMARY This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence
High dimensional graphs and variable selection with the Lasso
 ANNALS OF STATISTICS
, 2006
"... The pattern of zero entries in the inverse covariance matrix of a multivariate normal distribution corresponds to conditional independence restrictions between variables. Covariance selection aims at estimating those structural zeros from data. We show that neighborhood selection with the Lasso is a ..."
Abstract

Cited by 736 (22 self)
 Add to MetaCart
is a computationally attractive alternative to standard covariance selection for sparse highdimensional graphs. Neighborhood selection estimates the conditional independence restrictions separately for each node in the graph and is hence equivalent to variable selection for Gaussian linear models. We
Mixtures of Probabilistic Principal Component Analysers
, 1998
"... Principal component analysis (PCA) is one of the most popular techniques for processing, compressing and visualising data, although its effectiveness is limited by its global linearity. While nonlinear variants of PCA have been proposed, an alternative paradigm is to capture data complexity by a com ..."
Abstract

Cited by 532 (6 self)
 Add to MetaCart
maximumlikelihood framework, based on a specific form of Gaussian latent variable model. This leads to a welldefined mixture model for probabilistic principal component analysers, whose parameters can be determined using an EM algorithm. We discuss the advantages of this model in the context
Fast Bilateral Filtering for the Display of HighDynamicRange Images
, 2002
"... We present a new technique for the display of highdynamicrange images, which reduces the contrast while preserving detail. It is based on a twoscale decomposition of the image into a base layer, encoding largescale variations, and a detail layer. Only the base layer has its contrast reduced, the ..."
Abstract

Cited by 453 (10 self)
 Add to MetaCart
, thereby preserving detail. The base layer is obtained using an edgepreserving filter called the bilateral filter. This is a nonlinear filter, where the weight of each pixel is computed using a Gaussian in the spatial domain multiplied by an influence function in the intensity domain that decreases
A Scalable Statistical Static Timing Analyzer Incorporating Correlated NonGaussian and Gaussian Parameter Variations
"... We propose a scalable and efficient parameterized blockbased statistical static timing analysis (SSTA) algorithm incorporating both Gaussian and nonGaussian parameter distributions, capturing spatial correlations using a gridbased model. As a preprocessing step, we employ independent component an ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
We propose a scalable and efficient parameterized blockbased statistical static timing analysis (SSTA) algorithm incorporating both Gaussian and nonGaussian parameter distributions, capturing spatial correlations using a gridbased model. As a preprocessing step, we employ independent component
Statistical Timing Analysis with Correlated NonGaussian Parameters using Independent Component Analysis
 In ACM/IEEE International Workshop on Timing Issues
, 2006
"... We propose a scalable and efficient parameterized blockbased statistical static timing analysis algorithm incorporating both Gaussian and nonGaussian parameter distributions, capturing spatial correlations using a gridbased model. As a preprocessing step, we employ independent component analysis ..."
Abstract

Cited by 32 (3 self)
 Add to MetaCart
We propose a scalable and efficient parameterized blockbased statistical static timing analysis algorithm incorporating both Gaussian and nonGaussian parameter distributions, capturing spatial correlations using a gridbased model. As a preprocessing step, we employ independent component analysis
Results 1  10
of
8,437