probability density function

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probability density function

(Statistics) statistics a function representing the relative distribution of frequency of a continuous random variable from which parameters such as its mean and variance can be derived and having the property that its integral from a to b is the probability that the variable lies in this interval. Its graph is the limiting case of a histogram as the amount of data increases and the class intervals decrease in size. Also called: density function Compare cumulative distribution function, frequency distribution

probabil′ity den`sity func`tion

1. a function of a continuous variable whose integral over a region gives the probability that a random variable falls within the region.
2. a function of a discrete variable whose sum over a discrete set gives the probability of occurrence of a specified value.
References in periodicals archive ?
Figure 1 show the graph of the gamma[(1 - Exp)/Exp] distribution probability density functions and cumulative distribution, for some values of the parameters.
Estimation of wind energy potential using different probability density functions.
These neurons receive various classes of probability density functions coming from the summation layer, and produce a 1 (positive identification) for the chosen class with the maximum function value (owning the evaluation grade corresponding to the sample for identification) and a 0 (negative identification) for non-targeted classes.
For probability density functions, the probability P of an event occurring between two bounds is a and b is
From the families of multidimensional probability distributions described in literature [2], [3] we chose the Gaussian, Dirichlet and gamma distributions for analysis because their probability density functions are easy to use in analytical expressions.
Corresponding to these probability density functions, Hermite orthogonal polynomial [H.
In real world applications, the probability density functions of those quantities can only be estimated from physical measurements.
This part develops empirical likelihood inference methods for regression discontinuity designs, continuity or discontinuity of probability density functions, volatility measurement in high frequency financial data, and testing for partially identified moment inequality models.
Estimation of distribution parameters precedes modeling and the most commonly used probability density functions can be moved, stretched, shaped, altered, or any combination of these features, using up to 3 parameters.
The best-fitting model from the likelihood-based GLMM was used to estimate Bayesian posterior probability density functions for each model parameter, which were then combined with the GIS output to estimate probability density functions for total geoduck abundance in each region of Hood Canal.
it cannot be exactly defined in advance, thus the development of the actual utilization demand and of stocks can be determined only with random variables that are represented on the figure by the probability density functions of the standard normal distribution.

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