Content-Length: 129216 | pFad | http://en.m.wikipedia.org/wiki/Schilder%27s_theorem

Schilder's theorem - Wikipedia

In mathematics, Schilder's theorem is a generalization of the Laplace method from integrals on to functional Wiener integration. The theorem is used in the large deviations theory of stochastic processes. Roughly speaking, out of Schilder's theorem one gets an estimate for the probability that a (scaled-down) sample path of Brownian motion will stray far from the mean path (which is constant with value 0). This statement is made precise using rate functions. Schilder's theorem is generalized by the Freidlin–Wentzell theorem for Itō diffusions.

Statement of the theorem

edit

Let C0 = C0([0, T]; Rd) be the Banach space of continuous functions   such that  , equipped with the supremum norm ||⋅|| and   be the subspace of absolutely continuous functions whose derivative is in   (the so-called Cameron-Martin space). Define the rate function

 

on   and let   be two given functions, such that   (the "action") has a unique minimum  .

Then under some differentiability and growth assumptions on   which are detailed in Schilder 1966, one has

 

where   denotes expectation with respect to the Wiener measure   on   and   is the Hessian of   at the minimum  ;   is meant in the sense of an   inner product.

Application to large deviations on the Wiener measure

edit

Let B be a standard Brownian motion in d-dimensional Euclidean space Rd starting at the origen, 0 ∈ Rd; let W denote the law of B, i.e. classical Wiener measure. For ε > 0, let Wε denote the law of the rescaled process εB. Then, on the Banach space C0 = C0([0, T]; Rd) of continuous functions   such that  , equipped with the supremum norm ||⋅||, the probability measures Wε satisfy the large deviations principle with good rate function I : C0 → R ∪ {+∞} given by

 

if ω is absolutely continuous, and I(ω) = +∞ otherwise. In other words, for every open set G ⊆ C0 and every closed set F ⊆ C0,

 

and

 

Example

edit

Taking ε = 1/c2, one can use Schilder's theorem to obtain estimates for the probability that a standard Brownian motion B strays further than c from its starting point over the time interval [0, T], i.e. the probability

 

as c tends to infinity. Here Bc(0; ||⋅||) denotes the open ball of radius c about the zero function in C0, taken with respect to the supremum norm. First note that

 

Since the rate function is continuous on A, Schilder's theorem yields

 

making use of the fact that the infimum over paths in the collection A is attained for ω(t) = t/T . This result can be heuristically interpreted as saying that, for large c and/or large T

 

In fact, the above probability can be estimated more precisely: for B a standard Brownian motion in Rn, and any T, c and ε > 0, we have:

 

References

edit
  • Dembo, Amir; Zeitouni, Ofer (1998). Large deviations techniques and applications. Applications of Mathematics (New York) 38 (Second ed.). New York: Springer-Verlag. pp. xvi+396. ISBN 0-387-98406-2. MR 1619036. (See theorem 5.2)








ApplySandwichStrip

pFad - (p)hone/(F)rame/(a)nonymizer/(d)eclutterfier!      Saves Data!


--- a PPN by Garber Painting Akron. With Image Size Reduction included!

Fetched URL: http://en.m.wikipedia.org/wiki/Schilder%27s_theorem

Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy