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In probability theory and statistics, the exponential distribution or negative exponential distribution is the probability distribution of the distance between events in a Poisson point process, i.e., a process in which events occur continuously and independently at a constant average rate; the distance parameter could be any meaningful mono-dimensional measure of the process, such as time ...
An exGaussian random variable Z may be expressed as Z = X + Y, where X and Y are independent, X is Gaussian with mean μ and variance σ 2, and Y is exponential of rate λ. It has a characteristic positive skew from the exponential component. It may also be regarded as a weighted function of a shifted exponential with the weight being a ...
The Erlang distribution is the distribution of the sum of k independent and identically distributed random variables, each having an exponential distribution. The long-run rate at which events occur is the reciprocal of the expectation of X , {\displaystyle X,} that is, λ / k . {\displaystyle \lambda /k.}
The Weibull distribution can be characterized as the distribution of a random variable such that the random variable X = ( W λ ) k {\displaystyle X=\left({\frac {W}{\lambda }}\right)^{k}} is the standard exponential distribution with intensity 1.
Conversely, if X is a lognormal (μ, σ 2) random variable then log X is a normal (μ, σ 2) random variable. If X is an exponential random variable with mean β, then X 1/γ is a Weibull (γ, β) random variable. The square of a standard normal random variable has a chi-squared distribution with one degree of freedom.
The hypoexponential is a series of k exponential distributions each with their own rate , the rate of the exponential distribution. If we have k independently distributed exponential random variables X i {\displaystyle {\boldsymbol {X}}_{i}} , then the random variable,
The only continuous random variable that is memoryless is the exponential random variable. It models random processes like time between consecutive events. [8] The memorylessness property asserts that the amount of time since the previous event has no effect on the future time until the next event occurs.
The difference between two independent identically distributed exponential random variables is governed by a Laplace distribution, as is a Brownian motion evaluated at an exponentially distributed random time [citation needed]. Increments of Laplace motion or a variance gamma process evaluated over the time scale also have a Laplace distribution.