Probability density function of self-similar processes
Time-dependent probability density function in cubic stochastic processes
... [see. Eq. (62)] and also seen in Fig. 2 that the final adjustment time scale for the cubic process scales as d −1/2 . IV. CONCLUSION We have presented time-dependent PDFs in a cubic nonlinear stochastic process where the ...
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Time-dependent probability density function in cubic stochastic processes
... where κ is an arbitrary constant. Taking κ = 1 would correspond to a δ-function initial condition, whereas κ = 1 − 10 − 8 /2d corresponds to the actual initial condition (64). Fig. 2 shows how the peak amplitudes ...
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The Lamperti transformation for self-similar processes
... which is well defined for 0 < H < 1 and 0 < α ≤ 2. In order to approximate the integral, we use the method introduced by Mandelbrot and Wallis [8] replacing a sequence of Gaussian with α-stable random variables. In Fig. ...
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Improving the Accuracy of the Probability Density Function Estimation
... On the other hand, the structure and accuracy of constructed estimations are similar to kernel methods. It is important, that using the error estimates and Richardson’s extrapolation succeeded to build a ...
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Generative Probability Density Model in the Self-Organizing Map
... generative probability density model with the SOM enables comparison of the SOM and other similar methods, like the Gen erative Topographic ...posterior probability of the kernels given one ...
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Probability density function : An arbitrary continuous random variable X is similarly described by its probability density function f x = f X
... is similar to the situation for geometric random variables where the probability 1/6 that a roll of a six sided fair die produces a 3 (success) we can regard as the rate at which successes (3's) happen per ...
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Methods for analysis of functionals on Gaussian self similar processes
... nel function associated with reproducing kernel Hilbert space of a fractional Brownian motion process can be shown to be a Hilbert-Schmidt operator, then via Sazanov’s theorem (Sazonov 1958) there is a countably ...
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Parisian ruin of self-similar Gaussian risk processes
... OF SELF-SIMILAR GAUSSIAN RISK PROCESSES KRZYSZTOF DE ¸ BICKI, ENKELEJD HASHORVA, AND LANPENG JI Abstract: In this paper we derive the exact asymptotics of the probability of Parisian ruin for ...
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Self similar Markov processes and the time inversion property
... Markov processes on R enjoying the time inversion property (Definition 4), subject to their semigroup densities being absolutely continuous with respect to the Lebesgue ...semigroup density, we employ the ...
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Probability density function estimation using orthogonal forward regression
... kernel density estimation technique is proposed in [7]. Similar to the SVM methods, this technique employs the full data sample set as the kernel set and tries to make as many kernel weights to (near) zero ...
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Robust Scale Estimation for the Generalized Gaussian Probability Density Function
... the estimate. Weights are then a function of the residuals. The second approach consists in using sampling strategies and in estimating from several randomly selected subsets of observations. The final selection ...
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On the Method of Logarithmic Cumulants for Parametric Probability Density Function Estimation
... Fig. 4. Plot of the y = Ψ 3 (1, κ)/Ψ 2 (2, κ) function (solid), and diagonal y = κ (dashed) which characterizes its asymptotic slope. averages and the mean square errors (MSE) of the obtained estimates (compared ...
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The Probability Density Function of Interest Rates Implied in the Price of Options
... options. Probability distributions are estimated through the Söderlind and Svensson model adapted for yield option pricing (see footnote 5), for the same official rate reductions as for long term rates; results ...
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Probability distribution function for self-organization of shear flows
... that similar coherent structures (ramps) have also been successfully used in the prediction of the intermittent PDF tails of (positive) velocity gradient in Burgers turbulence [16, 17] that agree with numerical ...
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From infinite urn schemes to self-similar stable processes
... Consider the following infinite urn scheme. Suppose there is an infinite number of urns labeled by N = {1, 2, . . . }, all initially empty. Balls are thrown into the urns randomly one after another. At each round, a ball ...
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Negative-free approximation of probability density function for nonlinear projection filter
... As it is emphasised, however, in [21], the approximated pdf might not satisfy all requirements for any pdf to content. The integration of pdf over whole sampling space must be equal to 1. This condition can be easily met ...
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A multienvironment conditional probability density function model for turbulent reacting flows
... Fluctuations in the scalar dissipation rate are known to be significant in turbulent flows, 8 and large fluctuations can lead to local extinction in nonisothermal reacting flows. 5,7 Once extinguished, local fluid ...
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Deep-based conditional probability density function forecasting of residential loads
... i) Cluster/classify similar customers in terms of the number of the days/weather to reduce the variance of the uncertainty. The performance of the cluster/classification based approaches are highly dependent on ...
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Parameterizing deep convection using the assumed probability density function method
... are similar, with the exception of LBA, which, with four sample points, does not produce surface precipitation, thereby per- mitting large amounts of cloud water to remain ...
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Some Convergence Results On Stable Infinite Moving Average Processes And Stable Self-Similar Processes
... average processes with i.i.d. stable noise. We show that for such processes, a collection of weighted integrals of the periodogram, considered as a function-indexed stochastic process, converges ...
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