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Gaussian distribution
, 2010
"... We produce a collection of families of curves, whose point count statistics over Fp becomes Gaussian for p fixed. In particular, the average number of Fp points on curves in these families tends to infinity. 1 ..."
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We produce a collection of families of curves, whose point count statistics over Fp becomes Gaussian for p fixed. In particular, the average number of Fp points on curves in these families tends to infinity. 1
The rectified gaussian distribution
 In Proc. of NIPS
, 1998
"... A simple but powerful modification of the standard Gaussian distribution is studied. The variables of the rectified Gaussian are constrained to be nonnegative, enabling the use of nonconvex energy functions. Two multimodal examples, the competitive and cooperative distributions, illustrate the rep ..."
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Cited by 38 (2 self)
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A simple but powerful modification of the standard Gaussian distribution is studied. The variables of the rectified Gaussian are constrained to be nonnegative, enabling the use of nonconvex energy functions. Two multimodal examples, the competitive and cooperative distributions, illustrate
Warmup: Gaussian distributions
, 2009
"... Gaussian distribution of a single realvalued variable with mean µ ∈ R and variance σ2: N(x µ,σ 2) = 1 √ exp − ..."
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Gaussian distribution of a single realvalued variable with mean µ ∈ R and variance σ2: N(x µ,σ 2) = 1 √ exp −
The Recti ed Gaussian Distribution
"... A simple but powerful modi cation of the standard Gaussian distribution is studied. The variables of the recti ed Gaussian are constrained to be nonnegative, enabling the use of nonconvex energy functions. Two multimodal examples, the competitive and cooperative distributions, illustrate the represe ..."
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A simple but powerful modi cation of the standard Gaussian distribution is studied. The variables of the recti ed Gaussian are constrained to be nonnegative, enabling the use of nonconvex energy functions. Two multimodal examples, the competitive and cooperative distributions, illustrate
ON NONLINEAR TRANSFORMATIONS OF GAUSSIAN DISTRIBUTIONS
"... The unscented Kalman filter (UKF) relies on the unscented transformation (UT) that fits a Gaussian distribution to nonlinearly transformed so called sigma points. This contribution firstly gives the exact first and second order moments of the nonlinear transformation as a function of the rest ter ..."
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The unscented Kalman filter (UKF) relies on the unscented transformation (UT) that fits a Gaussian distribution to nonlinearly transformed so called sigma points. This contribution firstly gives the exact first and second order moments of the nonlinear transformation as a function of the rest
Gaussian processes for machine learning
, 2003
"... 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 hyperparameters us ..."
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Cited by 720 (2 self)
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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 hyperparameters
NonGaussian Distribution For Var
"... In this paper we compare di#erent approaches to compute VaR for heavy tailed return series. Using data from the Italian market, we show that almost all the return series present statistically significant skewness and kurtosis. We implement (i) the stable models proposed by Rachev et al. (2000), ( ..."
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), (ii) an alternative to the Gaussian distributions based on a Generalized Error Distribution , (iii) a nonparametric model proposed by Li (1999). All the model are then submitted to backtest on outofsample data in order to assess their forecasting power. We observe that when the percentiles
Modefinding for mixtures of Gaussian distributions
 Dept. of Computer Science, University of Sheffield
, 1999
"... I consider the problem of finding all the modes of a mixture of multivariate Gaussian distributions, which has applications in clustering and regression. I derive exact formulas for the gradient and Hessian and give a partial proof that the number of modes cannot be more than the number of component ..."
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Cited by 50 (8 self)
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I consider the problem of finding all the modes of a mixture of multivariate Gaussian distributions, which has applications in clustering and regression. I derive exact formulas for the gradient and Hessian and give a partial proof that the number of modes cannot be more than the number
Inferring a Gaussian distribution
, 2000
"... A common question in statistical modeling is “which out of a continuum of models are likely to have generated this data? ” For the Gaussian class of models, this question can be answered completely and exactly. This paper derives the exact posterior distribution over the mean and variance of the gen ..."
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Cited by 9 (0 self)
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A common question in statistical modeling is “which out of a continuum of models are likely to have generated this data? ” For the Gaussian class of models, this question can be answered completely and exactly. This paper derives the exact posterior distribution over the mean and variance
Results 1  10
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198,653