continuous random variable

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con·tin·u·ous ran·dom var·i·a·ble

continuous variable that may randomly assume any value in its domain but any particular value has no probability of occurring, only a probability density.
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Distributions of discrete and continuous random variables frequently used in applications
Subsequent chapters cover countable and uncountable sample spaces, continuous random variables, functions of one and two random variables, conditional probabilities for countable sample spaces and continuous random variables, Bernoulli, geometric and Poisson processes, Brownian motion and white noise, and convergence of random variables.
With examples, illustrations and accessible text Stapleton describes discrete probability models, special discrete distributions, continuous random variables, special continuous and conditional distributions, moment generating functions and limit theory, estimation, testing of hypotheses, the multivariate normal (as well as chi-square, t and F distributions) nonparametric statistics, linear statistical models, and frequency data.
The topics are basics of probability, discrete and continuous random variables, statistics, hypothesis testing, simple regression, and nonparametric statistics.
His topics include elements of probability, generating discrete and continuous random variables, the multivariate normal distribution and copulas, the statistical analysis of simulated data, variance reduction techniques, and Markov Chain Monte Carlo methods.
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