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| In [[mathematics]], '''Schilder's theorem''' is a result in the [[large deviations theory]] of [[stochastic process]]es. Roughly speaking, Schilder's theorem gives 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 function]]s. Schilder's theorem is generalized by the [[Freidlin–Wentzell theorem]] for [[Itō diffusion]]s.
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| ==Statement of the theorem==
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| Let ''B'' be a standard Brownian motion in ''d''-[[dimension]]al [[Euclidean space]] '''R'''<sup>''d''</sup> starting at the origin, 0 ∈ '''R'''<sup>''d''</sup>; let '''W''' denote the [[law (stochastic processes)|law]] of ''B'', i.e. classical [[Wiener measure]]. For ''ε'' > 0, let '''W'''<sub>''ε''</sub> denote the law of the rescaled process (√''ε'')''B''. Then, on the [[Banach space]] ''C''<sub>0</sub> = ''C''<sub>0</sub>([0, ''T'']; '''R'''<sup>''d''</sup>) of continuous functions <math> f : [0,T] \longrightarrow \mathbf{R}^d</math> such that <math>f(0)=0</math>, equipped with the [[supremum norm]] ||·||<sub>∞</sub>, the [[probability measure]]s '''W'''<sub>''ε''</sub> satisfy the large deviations principle with good rate function ''I'' : ''C''<sub>0</sub> → '''R''' ∪ {+∞} given by
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| :<math>I(\omega) = \frac{1}{2} \int_{0}^{T} | \dot{\omega}(t) |^{2} \, \mathrm{d} t</math>
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| if ''ω'' is [[absolutely continuous]], and ''I''(''ω'') = +∞ otherwise. In other words, for every [[open set]] ''G'' ⊆ ''C''<sub>0</sub> and every [[closed set]] ''F'' ⊆ ''C''<sub>0</sub>,
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| :<math>\limsup_{\varepsilon \downarrow 0} \varepsilon \log \mathbf{W}_{\varepsilon} (F) \leq - \inf_{\omega \in F} I(\omega)</math>
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| :<math>\liminf_{\varepsilon \downarrow 0} \varepsilon \log \mathbf{W}_{\varepsilon} (G) \geq - \inf_{\omega \in G} I(\omega).</math>
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| ==Example==
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| Taking ''ε'' = 1 ⁄ ''c''<sup>2</sup>, 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
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| :<math>\mathbf{W} (C_{0} \setminus \mathbf{B}_{c} (0; \| \cdot \|_{\infty})) \equiv \mathbf{P} \big[ \| B \|_{\infty} > c \big],</math> | |
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| as ''c'' tends to infinity. Here '''B'''<sub>''c''</sub>(0; ||·||<sub>∞</sub>) denotes the [[open ball]] of radius ''c'' about the zero function in ''C''<sub>0</sub>, taken with respect to the [[supremum norm]]. First note that
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| :<math>\| B \|_{\infty} > c \iff \sqrt{\varepsilon} B \in A := \big\{ \omega \in C_{0} \big| | \omega(t) | > 1 \mbox{ for some } t \in [0, T] \big\}.</math>
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| Since the rate function is continuous on ''A'', Schilder's theorem yields
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| :<math>\lim_{c \to \infty} \frac{1}{c^{2}} \log \mathbf{P} \big[ \| B \|_{\infty} > c \big]</math> | |
| ::<math>= \lim_{\varepsilon \to 0} \epsilon \mathbf{P} \big[ \sqrt{\varepsilon} B \in A \big]</math>
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| ::<math>= - \inf \left\{ \left. \frac{1}{2} \int_{0}^{T} | \dot{\omega}(t) |^{2} \, \mathrm{d} t \right| \omega \in A \right\}</math>
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| ::<math>= - \frac{1}{2} \int_{0}^{T} \frac{1}{T^{2}} \, \mathrm{d} t</math>
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| ::<math>= - \frac{1}{2 T},</math>
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| 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''
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| :<math>\frac{1}{c^{2}} \log \mathbf{P} \big[ \| B \|_{\infty} > c \big] \approx - \frac{1}{2 T},</math> | |
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| or, in other words,
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| :<math>\mathbf{P} \big[ \| B \|_{\infty} > c \big] \approx \exp \left( - \frac{c^{2}}{2 T} \right).</math>
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| In fact, the above probability can be estimated more precisely as follows: for ''B'' a standard Brownian motion in '''R'''<sup>''n''</sup>, and any ''T'', ''c'' and ''ε'' > 0, it holds that
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| :<math>\mathbf{P} \left[ \sup_{0 \leq t \leq T} \big| \sqrt{\varepsilon} B_{t} \big| \geq c \right] \leq 4 n \exp \left( - \frac{c^{2}}{2 n T \varepsilon} \right).</math>
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| ==References==
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| * {{cite book
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| | last= Dembo
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| | first = Amir
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| | coauthors = Zeitouni, Ofer
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| | title = Large deviations techniques and applications
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| | series = Applications of Mathematics (New York) 38
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| | edition = Second edition
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| | publisher = Springer-Verlag
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| | location = New York
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| | year = 1998
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| | pages = xvi+396
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| | isbn = 0-387-98406-2
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| | MR=1619036}} (See theorem 5.2)
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| [[Category:Asymptotic analysis]]
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| [[Category:Stochastic processes]]
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| [[Category:Probability theorems]]
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| [[Category:Large deviations theory]]
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