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Moment-generating FunctionIn probability theory and statistics, the moment-generating function of a random variable X is -
wherever this expectation exists. The moment-generating function generates the moments of the probability distribution, as follows. Provided the moment-generating function exists in an interval around t = 0, -
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If X has a continuous probability density function f(x) then the moment generating function is given by -
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where is the ith moment. Regardless of whether probability distribution is continuous or not, the moment-generating function is given by the Riemann-Stieltjes integral -
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where F is the cumulative distribution function. If X1, X2, ..., Xn is a sequence of independent (and not necessarily identically distributed) random variables, and -
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where the a i are constants, then the probability density function for S n is the convolution of the probability density functions of each of the X i and the moment-generating function for S n is given by -
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M_{S_n}(t)=M_{X_1}(a_1t)M_{X_2}(a_2t)\ldots M_{X_n}(a_nt). Related to the moment-generating function are a number of other transforms that are common in probability theory, including the characteristic function and the probability-generating function. The cumulant-generating function is the logarithm of the moment-generating function.
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