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In probability and statistics, the logarithmic distribution (also known as the logarithmic series distribution or the log-series distribution) is a discrete probability distribution derived from the Maclaurin series expansion = + + +.
In probability theory, a log-normal (or lognormal) distribution is a continuous probability distribution of a random variable whose logarithm is normally distributed. Thus, if the random variable X is log-normally distributed, then Y = ln( X ) has a normal distribution.
The geometric distribution, a discrete distribution which describes the number of attempts needed to get the first success in a series of independent Bernoulli trials, or alternatively only the number of losses before the first success (i.e. one less). The Hermite distribution; The logarithmic (series) distribution; The mixed Poisson distribution
In probability theory and statistics, the Exponential-Logarithmic (EL) distribution is a family of lifetime distributions with decreasing failure rate, defined on the interval [0, ∞). This distribution is parameterized by two parameters p ∈ ( 0 , 1 ) {\displaystyle p\in (0,1)} and β > 0 {\displaystyle \beta >0} .
The sum of probabilities + is a bit more involved to compute in logarithmic space, requiring the computation of one exponent and one logarithm. However, in many applications a multiplication of probabilities (giving the probability of all independent events occurring) is used more often than their addition (giving the probability of at least ...
Logarithmic can refer to: Logarithm, a transcendental function in mathematics; Logarithmic scale, the use of the logarithmic function to describe measurements; Logarithmic spiral, Logarithmic growth; Logarithmic distribution, a discrete probability distribution; Natural logarithm
List of logarithmic identities; Logarithm of a matrix; Logarithm table; Logarithmic addition; Logarithmic convolution; Logarithmic decrement; Logarithmic differentiation; Logarithmic distribution; Logarithmic growth; Logarithmic number system; Logarithmic Sobolev inequalities; Logarithmus; Logarithmus binaris; Logarithmus decadis; Logarithmus ...
the beta distribution if both shape parameters are ≥ 1, and the Weibull distribution if the shape parameter is ≥ 1. Note that all of the parameter restrictions have the same basic source: The exponent of non-negative quantity must be non-negative in order for the function to be log-concave.