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The exponential distribution is the special case of a Gamma distribution with shape parameter 1. If X ~ Exp (λ) and Xi ~ Exp (λ i) then: , closure under scaling by a positive factor. 1 + X ~ BenktanderWeibull (λ, 1), which reduces to a truncated exponential distribution. keX ~ Pareto ( k, λ). e−X ~ Beta (λ, 1).
The reciprocal 1/ X of a random variable X, is a member of the same family of distribution as X, in the following cases: Cauchy distribution, F distribution, log logistic distribution . Examples: If X is a Cauchy ( μ, σ) random variable, then 1/ X is a Cauchy ( μ / C, σ / C) random variable where C = μ2 + σ2.
The distribution may also be applicable in broader situations to model multivariate data with heavier tails than a normal distribution but finite moments. [ 1 ] The relationship between the exponential distribution and the Laplace distribution allows for a simple method for simulating bivariate asymmetric Laplace variables (including for the ...
A probability distribution is a mathematical description of the probabilities of events, subsets of the sample space. The sample space, often represented in notation by is the set of all possible outcomes of a random phenomenon being observed. The sample space may be any set: a set of real numbers, a set of descriptive labels, a set of vectors ...
The normal distribution is perhaps the most important case. Because the normal distribution is a location-scale family, its quantile function for arbitrary parameters can be derived from a simple transformation of the quantile function of the standard normal distribution, known as the probit function. Unfortunately, this function has no closed ...
A single-parameter exponential family is a set of probability distributions whose probability density function (or probability mass function, for the case of a discrete distribution) can be expressed in the form. where and are known functions. The function must be non-negative. An alternative, equivalent form often given is.
Memorylessness. In probability and statistics, memorylessness is a property of certain probability distributions. It describes situations where the time you've already waited for an event doesn't affect how much longer you'll have to wait. To model memoryless situations accurately, we have to disregard the past state of the system – the ...
Variance is a measure of dispersion, meaning it is a measure of how far a set of numbers is spread out from their average value. It is the second central moment of a distribution, and the covariance of the random variable with itself, and it is often represented by , , , , or . [ 1]