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Probability density function fit
Il Nuovo Cimento B, 1993A statistical method to test the level of agreement between random-variable measurements and a given theoretical probability density function is described.
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Sinepower probability density function
Journal of Hydrology, 1976As interesting new probability density function has been formulated to fit random data bounded on both sides, and which has a single mode within its range of values. A numerical example illustrates its application.
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Deriving parameter probability density functions
Reliability Engineering & System Safety, 1993Abstract The long-term performance of a nuclear fuel waste disposal system is typically studied by modelling potential releases of contaminants to the environment and the consequent health risk to humans. To deal with uncertainty in the estimated consequences of the releases, the behaviour of the disposal system may be repeatedly simulated under the ...
M.E. Stephens, B.W. Goodwin, T.H. Andres
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Correlation functions from probability densities
Computer Physics Communications, 1987Correlation functions are calculated from histograms of probability densities. This method allows to analyse correlations of higher moments from the data. At least for exploratory studies it is by far more practical than direct calculation of the correlations of higher moments during the simulation.
Berg, Bernd A., Devoto, Alberto
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Probability Density Functions and Kernel Density Estimation
2015Stochastic modeling loop in the stochastic optimization framework involves dealing with evaluation of a probabilistic objective function and constraints from the output data. Probability density functions (PDFs) are a fundamental tool used to characterize uncertain data. Equation 3.1 shows the definition of a PDF f of variable X.
Urmila Diwekar, Amy David
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PROBABILITY DENSITY FUNCTION LEARNING BY UNSUPERVISED NEURONS
International Journal of Neural Systems, 2001In a recent work, we introduced the concept of pseudo-polynomial adaptive activation function neuron (FAN) and presented an unsupervised information-theoretic learning theory for such structure. The learning model is based on entropy optimization and provides a way of learning probability distributions from incomplete data.
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Extended sinepower probability density function
Journal of Hydrology, 1978The author developed a new probability density function to fit random data, bounded on both extremes and having a single mode within its range of values. This function called sinepower probability density function (SP-PDF) has now been extended to include cases of minimum value having a non-zero probability.
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Calibration of Probability Density Function
2019The capability performance index (Cpk) is often used to measure the capability of the production process and to predict yield. However, this Cpk is only defined for the Gaussian distribution. At NXP Semiconductors an on-chip calibration technique is frequently used to reduce the effect of process variations.
Jos J. Dohmen +5 more
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Conditional Probability Density Functions
2012A discussion of conditional probability mass functions (PMFs) was given in Chapter 8. The motivation was that many problems are stated in a conditional format so that the solution must naturally accommodate this conditional structure. In addition, the use of conditioning is useful for simplifying probability calculations when two random variables are ...
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Appendix A: Probability Density Functions
2009This chapter contains sections titled: Relationship Between PDF and CDF PDFs Used in This Book Central Limit Theorem Generation of Standard Normal Random ...
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