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Free Poisson distribution : ウィキペディア英語版
Free Poisson distribution

In the mathematics of free probability theory, the free Poisson distribution is a counterpart of the Poisson distribution in conventional probability theory.
==Definition==
The free Poisson distribution〔Free Random Variables by D. Voiculescu, K. Dykema, A. Nica, CRM Monograph Series, American Mathematical Society, Providence RI, 1992〕 with jump size \alpha and rate \lambda arises in free probability theory as the limit of repeated free convolution
:
\left( \left(1-\frac\right)\delta_0 + \frac\delta_\alpha\right)^
as ''N'' → ∞.
In other words, let X_N be random variables so that X_N has value \alpha with probability \frac and value 0 with the remaining probability. Assume also that the family X_1,X_2,\ldots are freely independent. Then the limit as N\to\infty of the law of X_1+\cdots +X_N
is given by the Free Poisson law with parameters \lambda,\alpha.
This definition is analogous to one of the ways in which the classical Poisson distribution is obtained from a (classical) Poisson process.
The measure associated to the free Poisson law is given by
:\mu=\begin (1-\lambda) \delta_0 + \lambda \nu,& \text 0\leq \lambda \leq 1 \\
\nu, & \text\lambda >1,
\end

where
: \nu = \frac\sqrt \, dt
and has support ((1-\sqrt)^2,\alpha (1+\sqrt)^2 ).
This law also arises in random matrix theory as the Marchenko–Pastur law. Its free cumulants are all equal to \lambda.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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