Segmentation of Stochastic Images using ... - Jacobs University
Segmentation of Stochastic Images using ... - Jacobs University
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 />
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