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Segmentation of Stochastic Images using ... - Jacobs University

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3.2 <strong>Stochastic</strong> Partial Differential Equations<br />

domain in IR d , (Ω,A ,Π) a complete probability space, and a : Ω× ¯D → IR a stochastic function with<br />

continuous and bounded covariance function that satisfies ∃a min ,a max ∈ (0,∞) with<br />

P(ω ∈ Ω : a(x,ω) ∈ [a min ,a max ],∀x ∈ ¯D) = 1 , (3.8)<br />

i.e. the diffusion coefficient is bounded away from zero and infinity for realizations ω ∈ Ω almost<br />

sure. In addition, let f : Ω × ¯D → IR be a stochastic function that satisfies<br />

∫ ∫<br />

( ∫ )<br />

f 2 (x,ω)dxdω = E f 2 (x,ω)dx < ∞ . (3.9)<br />

Ω D<br />

D<br />

Then the elliptic SPDE analog to 3.7 reads<br />

−∇ · (a(ω,·)∇u(ω,·)) = f (ω,·)<br />

almost sure on D<br />

u(·) = g(·) on ∂D .<br />

Applying this concept to other PDEs yields parabolic and hyperbolic SPDEs.<br />

3.2.1 Existence and Uniqueness <strong>of</strong> Solutions for Elliptic SPDEs<br />

(3.10)<br />

The pro<strong>of</strong> <strong>of</strong> the existence and uniqueness <strong>of</strong> solutions for elliptic SPDEs is closely related to the<br />

existence and uniqueness pro<strong>of</strong> <strong>of</strong> the classical problem. The Lax-Milgram theorem [37] is applicable<br />

in the stochastic context when we show continuity and coercivity <strong>of</strong> the related linear and<br />

bilinear forms. The main difficulty <strong>of</strong> the pro<strong>of</strong> is that the stochastic PDE requires the multiplication<br />

<strong>of</strong> stochastic quantities, because the expression a∇u has to be well-defined. For this, we introduce<br />

the Wick product [68, 155] and have to investigate conditions for its existence. Let us begin with<br />

notation for the definition <strong>of</strong> the Wick product. The presentation is based on [150]. In the following<br />

let {H α : α ∈ I}, where I is an index set, be an orthogonal basis <strong>of</strong> L 2 (Ω).<br />

Definition The Wick product <strong>of</strong> two random variables f ,g : Ω → IR is the formal series<br />

(<br />

f g = ∑ f α g β H α+β (ξ ) = ∑ ∑ f α g β<br />

)H γ (ξ ) , (3.11)<br />

α,β<br />

γ α+β=γ<br />

whereas a random variable is expressed in the orthogonal basis via f = ∑ α f α H α (ξ ). The H α depend<br />

on a vector ξ = (ξ 1 ,...) <strong>of</strong> basic random variables.<br />

The Wick product is not well-defined for all second order random variables, i.e. L 2 (Ω) is not closed<br />

under Wick multiplication (see [150]). Therefore, we introduce restrictions <strong>of</strong> the space L 2 (Ω) to<br />

ensure a well-defined Wick multiplication.<br />

Definition The Kondratiev-Hilbert spaces S ρ,k [85] are<br />

{<br />

}<br />

(S ) ρ,k := f = ∑ α<br />

f α H α : f α ∈ IR for α ∈ I and ‖ f ‖ ρ,k < ∞<br />

where −1 ≤ ρ ≤ 1 and k ∈ IR. We define the norm ‖ · ‖ ρ,k via the scalar product<br />

and the expression (2N) α via<br />

, (3.12)<br />

( f ,g) ρ,k := ∑ α<br />

f α g α (α!) 1+ρ (2N) αk (3.13)<br />

(2N) α :=<br />

∞<br />

∏<br />

i=1<br />

(2 d β (i)<br />

1 β (i)<br />

2<br />

...β<br />

(i)<br />

d )α i<br />

. (3.14)<br />

The product ∏ ∞ i=1 is the product over all possible multi-indices β. Kondratiev spaces are separable<br />

Hilbert spaces [150].<br />

27

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