In probability theory, a log-normal distribution is a continuous probability distribution of a random variable whose logarithm is normally distributed. If X is a random variable with a normal distribution, then Y = exp(X) has a log-normal distribution; likewise, if Y is log-normally distributed, then X = log(Y) has a normal distribution. The log-normal distribution is the distribution of a random variable that takes only positive real values.
Log-normal is also written log normal or lognormal. The distribution is occasionally referred to as the Galton distribution or Galton's distribution, after Francis Galton. The log-normal distribution also has been associated with other names, such as McAlister, Gibrat and Cobb–Douglas.
A variable might be modeled as log-normal if it can be thought of as the multiplicative product of many independent random variables each of which is positive. (This is justified by considering the central limit theorem in the log-domain.) For example, in finance, the variable could represent the compound return from a sequence of many trades (each expressed as its return + 1); or a long-term discount factor can be derived from the product of short-term discount factors. In wireless communication, the attenuation caused by shadowing or slow fading from random objects is often assumed to be log-normally distributed: see log-distance path loss model.
The log-normal distribution is the maximum entropy probability distribution for a random variate X for which the mean and variance of is fixed.
Read more about Log-normal Distribution: μ and σ, Occurrence, Maximum Likelihood Estimation of Parameters, Multivariate Log-normal, Generating Log-normally Distributed Random Variates, Related Distributions, Similar Distributions
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