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443,783
in the Gaussian Approximation
, 1995
"... The O(N) symmetric scalar quantum field theory with λΦ 4 interaction is discussed in the Gaussian approximation. It is shown that the Goldstone theorem is fulfilled for arbitrary N. 1 ..."
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The O(N) symmetric scalar quantum field theory with λΦ 4 interaction is discussed in the Gaussian approximation. It is shown that the Goldstone theorem is fulfilled for arbitrary N. 1
The Variational Gaussian Approximation Revisited
, 2009
"... The variational approximation of posterior distributions by multivariate Gaussians has been much less popular in the Machine Learning community compared to the corresponding approximation by factorising distributions. This is for a good reason: the Gaussian approximation is in general plagued by an ..."
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Cited by 24 (0 self)
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The variational approximation of posterior distributions by multivariate Gaussians has been much less popular in the Machine Learning community compared to the corresponding approximation by factorising distributions. This is for a good reason: the Gaussian approximation is in general plagued
Conformal Gaussian Approximation
, 2008
"... We present an alternative way to determine the unknown parameter associated to a gaussian approximation in a generic twodimensional model. Instead of the standard variational approach, we propose a procedure based on a quantitative prediction of conformal invariance, valid for systems in the scaling ..."
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We present an alternative way to determine the unknown parameter associated to a gaussian approximation in a generic twodimensional model. Instead of the standard variational approach, we propose a procedure based on a quantitative prediction of conformal invariance, valid for systems
Testing the Gaussian approximation of aggregate traffic
, 2002
"... We search for methods or tools to detect whether the 1dimensional marginal distribution of traffic increments of aggregate TCPtraffic satisfy the hypothesis of approximate normality. Gaussian approximation requires a high level of aggregation in both "vertical" (source aggregation) and & ..."
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Cited by 43 (1 self)
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We search for methods or tools to detect whether the 1dimensional marginal distribution of traffic increments of aggregate TCPtraffic satisfy the hypothesis of approximate normality. Gaussian approximation requires a high level of aggregation in both "vertical" (source aggregation
Gaussian approximations of multiple integrals
 Electronic Communications in Probability 12
, 2007
"... Fix k ≥ 1, and let I(l), l ≥ 1, be a sequence of kdimensional vectors of multiple WienerItô integrals with respect to a general Gaussian process. We establish necessary and sufficient conditions to have that, as l → +∞, the law of I(l) is asymptotically close (for example, in the sense of Prokhoro ..."
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Cited by 7 (6 self)
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Fix k ≥ 1, and let I(l), l ≥ 1, be a sequence of kdimensional vectors of multiple WienerItô integrals with respect to a general Gaussian process. We establish necessary and sufficient conditions to have that, as l → +∞, the law of I(l) is asymptotically close (for example, in the sense
Gaussian approximation theorems for urn models and their
, 2002
"... We consider weak and strong Gaussian approximations for a twocolor generalized Friedman’s urn model with homogeneous and nonhomogeneous generating matrices. In particular, the functional central limit theorems and the laws of iterated logarithm are obtained. As an application, we obtain the asympto ..."
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Cited by 16 (5 self)
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We consider weak and strong Gaussian approximations for a twocolor generalized Friedman’s urn model with homogeneous and nonhomogeneous generating matrices. In particular, the functional central limit theorems and the laws of iterated logarithm are obtained. As an application, we obtain
◮ Gaussian approximation (Laplace)
"... g(t) = Hf(t)+ǫ(t), t ∈ [1,·· ·,T] g(r) = Hf(r)+ǫ(r), r = (x,y) ∈ R 2 ◮ f unknown quantity (input) ◮ H Forward operator: (Convolution, Radon, Fourier or any Linear operator) ◮ g observed quantity (output) ◮ ǫ represents the errors of modeling and measurement Discretization: g = Hf +ǫ ..."
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g(t) = Hf(t)+ǫ(t), t ∈ [1,·· ·,T] g(r) = Hf(r)+ǫ(r), r = (x,y) ∈ R 2 ◮ f unknown quantity (input) ◮ H Forward operator: (Convolution, Radon, Fourier or any Linear operator) ◮ g observed quantity (output) ◮ ǫ represents the errors of modeling and measurement Discretization: g = Hf +ǫ
Rough Interfaces Beyond the Gaussian Approximation
, 1994
"... We compare predictions of the Capillary Wave Model beyond its Gaussian approximation with Monte Carlo results for the energy gap and the surface energy of the 3D Ising model in the scaling region. Our study reveals that the finite size effects of these quantities are well described by the Capillary ..."
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We compare predictions of the Capillary Wave Model beyond its Gaussian approximation with Monte Carlo results for the energy gap and the surface energy of the 3D Ising model in the scaling region. Our study reveals that the finite size effects of these quantities are well described by the Capillary
Multiuser detection based on Gaussian approximation
 IN PROC. WORKSHOP ON TELECOMM. INTERNET AND SIGNAL PROC
, 2004
"... In this paper, a class of nonlinear MMSE multiuser detectors are derived based on a multivariate Gaussian approximation of the multiple access interference. This approach leads to identical expressions as describing the probabilistic data association (PDA) detector, thus providing an alternative an ..."
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Cited by 1 (0 self)
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In this paper, a class of nonlinear MMSE multiuser detectors are derived based on a multivariate Gaussian approximation of the multiple access interference. This approach leads to identical expressions as describing the probabilistic data association (PDA) detector, thus providing an alternative
Results 1  10
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443,783