non-Gaussian noise processes
Detecting nonlinearity in time series driven by non-Gaussian noise: the case of river flows
... The best predictions are no more found at the right extreme of the DVS plots (Fig. 2b) but the minimum of the predic- tion error is not as pronounced as it would be expected for a markedly nonlinear process. The problem ...
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Analysis of generalized Lévy white noise functionals
... white noise functionals, we define and study the generalized Le´vy white noise functionals by means of their functional representations acting on test ...white noise analysis initiated by T. Hida to ...
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Radar Signal Detection In Non-Gaussian Noise Using RBF Neural Network
... with non-Gaussian ...impulsive noise interference such as environmental effects of at- mospherics (lighting) and meteor train ...impulsive noise significantly reduces the signal detector ...
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Detecting periodicities with Gaussian processes
... to noise or ...the Gaussian process approach show a strong periodic signal (we have for all genes S = ...the Gaussian process models cannot simply be interpreted as ...fitting non sinusoidal ...
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Modeling of non Gaussian colored noise and application in CR multi sensor networks
... practical noise background. Gaussian white noise are typically used to model practical noise processes that affect digital sensing systems [1], such as the multi-radar system and under- ...
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On the Use of Second and Third Moments for the Comparison of Linear Gaussian and Simple Bilinear White Noise Processes
... white noise process is the linear Gaussian white noise ...linear Gaussian white noise process also plays significant role as a basic building block in the construction of linear and ...
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Gaussian Processes for Ordinal Regression
... Gaussian processes (O’Hagan, 1978; Neal, 1997) have provided a promising non-parametric Bayesian approach to metric regression (Williams and Rasmussen, 1996) and classification prob- lems (Williams ...
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Gaussian processes for computer experiments
... unknown, non-convex and potentially noisy function is at the center of many computer ex- ...centered Gaussian Process Y : D → R which allows to control the assumptions we put on the smoothness of the ...
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Sequential Parameter Estimation of Time Varying Non Gaussian Autoregressive Processes
... The results of the first experiment with time-varying AR parameters are shown in Figure 5. There, a was attributed a piecewise changing behavior where it jumped from 0.99 to 0.95 at the time instant t = 1001, and the ...
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Bayesian Warped Gaussian Processes
... output noise explicitly, these flat zones transfer and magnify output noise to latent space, with the consequent degradation in ...the non-stationary smoothness: Abrupt changes are followed by ...
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Differentially Private Gaussian Processes
... adding noise to the result, to mask the influence of individual ...to non-parametric models, such as histograms [Wasserman and Zhou, 2010] and other density estimators, such as the method described in Hall ...
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Extraction of Signals Buried in Noise: Non Ergodic Processes
... in non-ergodic ...a non-ergodic framework without averaging or smoothing in the direct time or frequency ...of noise, correlated or not with the signal, colored or white, Gaussian or not, and ...
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Non Markovianity hierarchy of Gaussian processes and quantum amplification
... one-mode Gaussian channels according to their non-Markovianity degree, using an intuitive pictorial diagram divided in three regions, one per each class of the hi- ...weakly non-Markovian ...
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Modelling Representation Noise in Emotion Analysis using Gaussian Processes
... We also compared our approach with two non- Bayesian approaches commonly used in the literature, ridge regression and support vector regression (SVR) with an SE kernel. For these models we used grid search to ...
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Study and analysis the BER performance of linear multiuser detectors in non-Gaussian noise channel
... white Gaussian noise(AWGN) ...with non-Gaussian noise remains an open ...external noise, there are no serious reasons for accurately determining its probability ...interference, ...
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Gaussian Processes in Machine Learning
... of this hierarchical specification is that it allows us to specify vague prior infor- mation in a simple way. For example, we’ve stated that we believe the function to be close to a second order polynomial, but we ...
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Gaussian Processes for Machine Learning
... residuals on the training set one can also obtain a predictive variance and thus get a MSLL value for LR. The rigid-body-dynamics (RBD) model has a number of free parameters; these were estimated by Vijayakumar et al. ...
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Survey on Noise Removal in MRI Brain Image for Various Filters
... Priyanka Punhani et al. [3], Magnetic Resonance Imaging is most popularly used techniques in clinical diagnosis. During acquisition, image quality is degraded by certain noise and artifacts. Due to which, it is ...
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Sparse Online Gaussian Processes
... è ¸ådëçî |îæçå øJæ ì¸ë'øJùdî iëçè¾îõd÷!äxéõiä1ø_ä¨ådæçèXé !øçùdî å ãÃðëçî ¸ëçîæçæçèXì¸éuïíeì¸éDøçè¾éOådì¸ådæ èXédîvñ¡údøJùdîcêdëçìxê9ì¸æÄîeõ æçêdäxëçæÄîãÃð ä ¸ìxëçèXøçùd÷ 'èXøçùä ,îe[r] ...
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String and Membrane Gaussian Processes
... As previously discussed the prediction scheme operated by the BCM is Kolmogorov-inconsistent in that the resulting predictive distributions are not consistent by marginalization. 15 Moreover, jointly predicting all ...
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