Results 1  10
of
713,347
Estimation in Gaussian Noise: Properties of the minimum meansquare error
 IEEE Trans. Inf. Theory
, 2011
"... Abstract—Consider the minimum meansquare error (MMSE) of estimating an arbitrary random variable from its observation contaminated by Gaussian noise. The MMSE can be regarded as a function of the signaltonoise ratio (SNR) as well as a functional of the input distribution (of the random variable t ..."
Abstract

Cited by 46 (13 self)
 Add to MetaCart
Abstract—Consider the minimum meansquare error (MMSE) of estimating an arbitrary random variable from its observation contaminated by Gaussian noise. The MMSE can be regarded as a function of the signaltonoise ratio (SNR) as well as a functional of the input distribution (of the random variable
Mutual information and minimum meansquare error in Gaussian channels
 IEEE TRANS. INFORM. THEORY
, 2005
"... This paper deals with arbitrarily distributed finitepower input signals observed through an additive Gaussian noise channel. It shows a new formula that connects the inputoutput mutual information and the minimum meansquare error (MMSE) achievable by optimal estimation of the input given the out ..."
Abstract

Cited by 285 (32 self)
 Add to MetaCart
This paper deals with arbitrarily distributed finitepower input signals observed through an additive Gaussian noise channel. It shows a new formula that connects the inputoutput mutual information and the minimum meansquare error (MMSE) achievable by optimal estimation of the input given
Minimum MeanSquare Error
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
Abstract
 Add to MetaCart
All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
Capacity of multiantenna Gaussian channels
 EUROPEAN TRANSACTIONS ON TELECOMMUNICATIONS
, 1999
"... We investigate the use of multiple transmitting and/or receiving antennas for single user communications over the additive Gaussian channel with and without fading. We derive formulas for the capacities and error exponents of such channels, and describe computational procedures to evaluate such form ..."
Abstract

Cited by 2878 (6 self)
 Add to MetaCart
We investigate the use of multiple transmitting and/or receiving antennas for single user communications over the additive Gaussian channel with and without fading. We derive formulas for the capacities and error exponents of such channels, and describe computational procedures to evaluate
Image denoising using a scale mixture of Gaussians in the wavelet domain
 IEEE TRANS IMAGE PROCESSING
, 2003
"... We describe a method for removing noise from digital images, based on a statistical model of the coefficients of an overcomplete multiscale oriented basis. Neighborhoods of coefficients at adjacent positions and scales are modeled as the product of two independent random variables: a Gaussian vecto ..."
Abstract

Cited by 514 (17 self)
 Add to MetaCart
coefficient reduces to a weighted average of the local linear estimates over all possible values of the hidden multiplier variable. We demonstrate through simulations with images contaminated by additive white Gaussian noise that the performance of this method substantially surpasses that of previously
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 704 (31 self)
 Add to MetaCart
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
Functional Properties of Minimum MeanSquare Error and Mutual Information
"... Abstract—In addition to exploring its various regularity properties, we show that the minimum meansquare error (MMSE) is a concave functional of the input–output joint distribution. In the case of additive Gaussian noise, the MMSE is shown to be weakly continuous in the input distribution and Lipsc ..."
Abstract

Cited by 9 (1 self)
 Add to MetaCart
Abstract—In addition to exploring its various regularity properties, we show that the minimum meansquare error (MMSE) is a concave functional of the input–output joint distribution. In the case of additive Gaussian noise, the MMSE is shown to be weakly continuous in the input distribution
Gaussian processes for machine learning
 in: Adaptive Computation and Machine Learning
, 2006
"... Abstract. We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn the hyperpar ..."
Abstract

Cited by 631 (2 self)
 Add to MetaCart
Abstract. We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn
1Functional Properties of Minimum Meansquare Error and Mutual Information
"... Abstract—In addition to exploring its various regularity properties, we show that the minimum meansquare error (MMSE) is a concave functional of the inputoutput joint distribution. In the case of additive Gaussian noise, the MMSE is shown to be weakly continuous in the input distribution and Lips ..."
Abstract
 Add to MetaCart
Abstract—In addition to exploring its various regularity properties, we show that the minimum meansquare error (MMSE) is a concave functional of the inputoutput joint distribution. In the case of additive Gaussian noise, the MMSE is shown to be weakly continuous in the input distribution
Results 1  10
of
713,347