Laplace principle (large deviations theory)

Laplace principle (large deviations theory)

In mathematics, Laplace's principle is a basic theorem in large deviations theory, similar to Varadhan's lemma. It gives an asymptotic expression for the Lebesgue integral of exp(−"θφ"("x")) over a fixed set "A" as "θ" becomes large. Such expressions can be used, for example, in statistical mechanics to determining the limiting behaviour of a system as the temperature tends to absolute zero.

tatement of the result

Let "A" be a Lebesgue-measurable subset of "d"-dimensional Euclidean space R"d" and let "φ" : R"d" → R be a measurable function with

:int_{A} e^{- varphi(x)} , mathrm{d} x < + infty.

Then

:lim_{ heta o + infty} frac1{ heta} log int_{A} e^{- heta varphi(x)} , mathrm{d} x = - mathop{mathrm{ess , inf_{x in A} varphi(x),

where ess inf denotes the essential infimum. Heuristically, this may be read as saying that for large "&theta;",

:int_{A} e^{- heta varphi(x)} , mathrm{d} x approx exp left( - heta mathop{mathrm{ess , inf_{x in A} varphi(x) ight).

Application

The Laplace principle can be applied to the family of probability measures P"&theta;" given by

:mathbf{P}_{ heta} (A) = left( int_{A} e^{- heta varphi(x)} , mathrm{d} x ight) Big/ left( int_{mathbf{R}^{d e^{- heta varphi(y)} , mathrm{d} y ight)

to give an asymptotic expression for the probability of some set/event "A" as "&theta;" becomes large. For example, if "X" is a standard normally distributed random variable on R, then

:lim_{varepsilon downarrow 0} varepsilon log mathbf{P} ig [ sqrt{varepsilon} X in A ig] = - mathop{mathrm{ess , inf_{x in A} frac{x^{2{2}

for every measurable set "A".

References

* cite book
last= Dembo
first = Amir
coauthors = Zeitouni, Ofer
title = Large deviations techniques and applications
series = Applications of Mathematics (New York) 38
edition = Second edition
publisher = Springer-Verlag
location = New York
year = 1998
pages = xvi+396
isbn = 0-387-98406-2
MathSciNet|id=1619036


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