Results 1  10
of
1,096,520
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
Robust MeanSquared Error Estimation of . . .
, 2004
"... This paper is a continuation of the work in [11] and [2] on the problem of estimating by a linear estimator, N unobservable input vectors, undergoing the same linear transformation, from noisecorrupted observable output vectors. Whereas in the aforementioned papers, only the matrix representing the ..."
Abstract
 Add to MetaCart
the linear transformation was assumed uncertain, here we are concerned with the case in which the second order statistics of the noise vectors (i.e., their covariance matrices) are also subjected to uncertainty. We seek a robust meansquared error estimator immuned against both sources of uncertainty. We
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.
MEANSQUARE ERRORS OF ESTIMATORS:
"... Suppose we have a parametric family of probability distributions with a likelihood function f(x, θ) for one observation, where f(x, θ) is a probability mass function for a discrete distribution or a probability density function for a continuous distribution. Let Eθ denote expectation, and Pθ probabi ..."
Abstract
 Add to MetaCart
probability, when θ is the true value of the parameter. Let X = (X1,..., Xn) be a vector of i.i.d. observations with distribution Pθ. Suppose g = g(θ) is a realvalued function of the parameter θ. One criterion for choosing an estimator T = T(X) of g(θ) is to minimize the meansquared error (MSE) Eθ((T(X) − g
Robust MeanSquared Error Estimation of Multiple . . .
, 2005
"... This paper is a continuation of the work in [11] and [2] on the problem of estimating by a linear estimator, N unobservable input vectors, undergoing the same linear transformation, from noisecorrupted observable output vectors. Whereas in the aforementioned papers, only the matrix representing t ..."
Abstract

Cited by 15 (5 self)
 Add to MetaCart
the linear transformation was assumed uncertain, here we are concerned with the case in which the second order statistics of the noise vectors (i.e., their covariance matrices) are also subjected to uncertainty. We seek a robust meansquared error estimator immuned against both sources of uncertainty. We
Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization
, 1993
"... The paper describes a rankbased fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The fitness assignment method is then modified to a ..."
Abstract

Cited by 610 (15 self)
 Add to MetaCart
to allow direct intervention of an external decision maker (DM). Finally, the MOGA is generalised further: the genetic algorithm is seen as the optimizing element of a multiobjective optimization loop, which also comprises the DM. It is the interaction between the two that leads to the determination of a
ModelBased Clustering, Discriminant Analysis, and Density Estimation
 JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
, 2000
"... Cluster analysis is the automated search for groups of related observations in a data set. Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures and most clustering methods available in commercial software are also of this type. However, there is little ..."
Abstract

Cited by 557 (28 self)
 Add to MetaCart
recovery from noisy data, and spatial density estimation. Finally, we mention limitations of the methodology, a...
Good ErrorCorrecting Codes based on Very Sparse Matrices
, 1999
"... We study two families of errorcorrecting codes defined in terms of very sparse matrices. "MN" (MacKayNeal) codes are recently invented, and "Gallager codes" were first investigated in 1962, but appear to have been largely forgotten, in spite of their excellent properties. The ..."
Abstract

Cited by 741 (23 self)
 Add to MetaCart
We study two families of errorcorrecting codes defined in terms of very sparse matrices. "MN" (MacKayNeal) codes are recently invented, and "Gallager codes" were first investigated in 1962, but appear to have been largely forgotten, in spite of their excellent properties
Results 1  10
of
1,096,520