Search results
Results from the WOW.Com Content Network
In probability theory and statistics, the moment-generating function of a real-valued random variable is an alternative specification of its probability distribution.Thus, it provides the basis of an alternative route to analytical results compared with working directly with probability density functions or cumulative distribution functions.
Pages in category "Generating functions" The following 12 pages are in this category, out of 12 total. ... Factorial moment generating function; G. Generating ...
There are various types of generating functions, including ordinary generating functions, exponential generating functions, Lambert series, Bell series, and Dirichlet series. Every sequence in principle has a generating function of each type (except that Lambert and Dirichlet series require indices to start at 1 rather than 0), but the ease ...
So the cumulant generating function is the logarithm of the moment generating function = (). The first cumulant is the expected value ; the second and third cumulants are respectively the second and third central moments (the second central moment is the variance ); but the higher cumulants are neither moments nor central moments, but ...
The moment generating function of a real random variable is the expected value of , as a function of the real parameter . For a normal distribution with density f {\displaystyle f} , mean μ {\displaystyle \mu } and variance σ 2 {\textstyle \sigma ^{2}} , the moment generating function exists and is equal to
Second moment method; Variance. Coefficient of variation; Variance-to-mean ratio; Covariance function; An inequality on location and scale parameters; Taylor expansions for the moments of functions of random variables; Moment problem. Hamburger moment problem. Carleman's condition; Hausdorff moment problem; Trigonometric moment problem ...
Besides the probability function, the cumulative distribution function, the probability mass function and the probability density function, the moment generating function and the characteristic function also serve to identify a probability distribution, as they uniquely determine an underlying cumulative distribution function. [10]
Julia provides package StableDistributions.jl which has methods of generation, fitting, probability density, cumulative distribution function, characteristic and moment generating functions, quantile and related functions, convolution and affine transformations of stable distributions. It uses modernised algorithms improved by John P. Nolan.