Search results
Results From The WOW.Com Content Network
The product of independent random variables X and Y may belong to the same family of distribution as X and Y: Bernoulli distribution and log-normal distribution. Example: If X 1 and X 2 are independent log-normal random variables with parameters (μ 1, σ 2 1) and (μ 2, σ 2 2) respectively, then X 1 X 2 is a log-normal random variable with ...
This relationship is true regardless of the base of the logarithmic or exponential function: If is normally distributed, then so is for any two positive numbers , . Likewise, if e Y {\displaystyle \ e^{Y}\ } is log-normally distributed, then so is a Y , {\displaystyle \ a^{Y}\ ,} where 0 < a ≠ 1 {\displaystyle 0<a\neq 1} .
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} .
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 ...
When the logarithm of a random variable has a normal distribution, the variable is said to have a log-normal distribution. [75] Log-normal distributions are encountered in many fields, wherever a variable is formed as the product of many independent positive random variables, for example in the study of turbulence. [76]
A Poisson compounded with Log(p)-distributed random variables has a negative binomial distribution. In other words, if N is a random variable with a Poisson distribution , and X i , i = 1, 2, 3, ... is an infinite sequence of independent identically distributed random variables each having a Log( p ) distribution, then
The log-likelihood is also particularly useful for exponential families of distributions, which include many of the common parametric probability distributions. The probability distribution function (and thus likelihood function) for exponential families contain products of factors involving exponentiation. The logarithm of such a function is a ...
The natural exponential family of a distribution may be realized by shifting or translating K(t), and adjusting it vertically so that it always passes through the origin: if f is the pdf with cumulant generating function () = (), and | is its natural exponential family, then () = (), and () = (+) ().