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Gaussian measure : ウィキペディア英語版
Gaussian measure

In mathematics, Gaussian measure is a Borel measure on finite-dimensional Euclidean space R''n'', closely related to the normal distribution in statistics. There is also a generalization to infinite-dimensional spaces. Gaussian measures are named after the German mathematician Carl Friedrich Gauss. One reason why Gaussian measures are so ubiquitous in probability theory is the Central Limit Theorem. Loosely speaking, it states that if a random variable
''X'' is obtained by summing a large number ''N'' of independent random variables of order 1, then ''X'' is of order \sqrt and its law is
approximately Gaussian.
==Definitions==
Let ''n'' ∈ N and let ''B''0(R''n'') denote the completion of the Borel ''σ''-algebra on R''n''. Let ''λ''''n'' : ''B''0(R''n'') → (+∞ ) denote the usual ''n''-dimensional Lebesgue measure. Then the standard Gaussian measure ''γ''''n'' : ''B''0(R''n'') → (1 ) is defined by
:\gamma^ (A) = \frac} \int_ \exp \left( - \frac \| x \|_}^ \right) \, \mathrm \lambda^ (x)
for any measurable set ''A'' ∈ ''B''0(R''n''). In terms of the Radon–Nikodym derivative,
:\frac}} (x) = \frac} \exp \left( - \frac \| x \|_}^ \right).
More generally, the Gaussian measure with mean ''μ'' ∈ R''n'' and variance ''σ''2 > 0 is given by
:\gamma_ (A) := \frac} \int_ \exp \left( - \frac^}^ \right) \, \mathrm \lambda^ (x).
Gaussian measures with mean ''μ'' = 0 are known as centred Gaussian measures.
The Dirac measure ''δ''''μ'' is the weak limit of \gamma_ as ''σ'' → 0, and is considered to be a degenerate Gaussian measure; in contrast, Gaussian measures with finite, non-zero variance are called non-degenerate Gaussian measures.

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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