Logmoment generating function

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In mathematics, the logarithmic momentum generating function (equivalent to cumulant generating function) (logmoment gen func) is defined as follows:

$\mu_{Y}(s)=\ln E(e^{s\cdot Y})$

where Y is a random variable.

Thus, if Y is a discrete random variable, then

$\mu_{Y}(s):=\ln \sum_y P(y)\cdot e^{s\cdot y} ,$

especially for the binary case (Bernoulli distribution)

$\mu_Y(s)=\ln\left\{p\cdot e^s + (1-p)\right\}$

and if Y is a random variable with continuous distribution, then

$\mu_{Y}(s):=\ln \int_y \Phi(y)\cdot e^{s\cdot y}.$

Here Φ is the cumulative distribution function of Y.

it is also true that for a sum of independent random variables

$Y=\sum_{j=1}^J X_j$

that

$\mu_Y(s)=\sum_{j=1}^J \mu_{X_j}(s)$

Proof:

$\mu_Y(s)=\ln \left(e^{s\cdot Y}\right) = \ln E\left(e^{s\cdot \sum_{j=1}^J X_j}\right) \stackrel{*}{=} \ln\prod_{j=1}^{J} E\left(e^{s\cdot X_j}\right) = \sum_{j=1}^J \ln E\left(e^{s\cdot X_j}\right) = \sum_{j=1}^J \mu_{X_j}(s).$

("*" is where we used the independence of the $X j$ random variables)

Category: Probability theory

Logmoment generating function

From Wikipedia, the free encyclopedia

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