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This probability density works much like an ordinary probability function.
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We show that, still retaining much of the general essence of the Central Limit Theorem, this process presents a functional attractor which is neither Gaussian nor Lévy like, and is precisely akin numerically to a probability density function shown in previous works to have ubiquitous character, namely the two-parameter beta distribution.
In this work, the probability density function of the absolute value of the prediction error is used.
Our model is probabilistic because it works with the probability density functions of the different inputs related to the natural history or detection of BC.
However, as indicated in earlier works [15], the Gaussian probability density function (PDF) may not be a perfect fit for curvelet data.
Thus, the Cauchy model is selected to approximate the probability density function of the AC coefficients in our work.
Works devoted to the estimation of probability density and hazard rate functions include the following.
The definition and governing equation of probability density function (PDF) have been implemented in the present work.
In this work, a non-parametric method for probability density function estimation based on kernel density estimation [7] is used.
SCE-based pilot allocation works based on the sampling from a Probability Density Function (PDF) which is updated in each iteration.
In this work kernel density estimation is used to generate probability density functions ( pdfs ) for bond length, bond angle and torsion angle histograms derived from the CSD.
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