probability density function


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Related to probability density function: Probability distribution function

probability density function

[‚präb·ə′bil·əd·ē ¦den·səd·ē ‚fəŋk·shən]
(statistics)
A real-valued function whose integral over any set gives the probability that a random variable has values in this set. Also known as density function; frequency function.
References in periodicals archive ?
7), which is solved with Navier-Stokes equations, and the exponential probability density function of pulsating intensity at the position, across all fluid particles in the profile, is shown as
The following graphical representations for Probability Density Function, Cumulative Distribution Function, Survival Function, Hazard Rate Function and Reverse Hazard Rate Function of DWD given below, with different shape and scale parameters.
By using the probability density function of narrowband peaks, Bendat [7] showed that the fatigue damage for a narrow band process can be found by
Investigation of the flame structure of Spray-A using the transported probability density function," 19th Australasian Fluid Mechanics Conference, Paper No.
where [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] denote the CDFs and the probability density function (PDF)s for [[beta].
1) is solved for each value assigned to the random variables to obtain results using the probability density function prescribed.
The significant piece of work, on the distribution, has been done under frequentist approach such as maximum-likelihood estimation, direct-sum decomposition principle, correlated Nakagami process, probability density function of the sum and the difference of two correlated squared Nakagami variates, backscatter analysis based on generalized entropies and neural function approximation, compressed logarithmic computation and bootstrap bias-corrected maximum likelihood estimation.
Statisticians call this a probability density function.
Models which assume the shape of probability density function are termed parametric.
Probability density function of a hyper-exponential distribution for k components is:
greater geoduck densities) than expected from the probability density function, and introduces a bias toward selecting more complicated models.
Aggarwal et al [7] proposed an outlier detection technique based on density, in which the data records were distributed in an uncertain region with a probability density function (PDF), where the value of PDF in this region is 1.

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