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searching for Covariance operator 9 found (21 total)

Functional correlation (1,521 words) [view diff] exact match in snippet view article find links to article

if and only if ρ 1 = 0 {\displaystyle \rho _{1}=0} . The cross-covariance operator for two random functions X {\displaystyle X} and Y {\displaystyle

Kosambi–Karhunen–Loève theorem (10,711 words) [view diff] exact match in snippet view article find links to article

\left\{Y[n]Y^{*}[m]\right\}} For any vector x[n] we denote by K the covariance operator represented by this matrix, E { | ⟨ Y , x ⟩ | 2 } = ⟨ K x , x ⟩ =

Differentiation of integrals (1,080 words) [view diff] exact match in snippet view article find links to article

some hope if one has good control over the covariance of γ. Let the covariance operator of γ be S : H → H given by ⟨ S x , y ⟩ = ∫ H ⟨ x , z ⟩ ⟨ y , z ⟩

Kernel embedding of distributions (9,770 words) [view diff] exact match in snippet view article find links to article

this joint embedding may be interpreted as an uncentered cross-covariance operator C X Y : H → H {\displaystyle {\mathcal {C}}_{XY}:{\mathcal {H}}\to

Algebra of random variables (3,557 words) [view diff] exact match in snippet view article find links to article

\operatorname {Cov} [X,Y]=\operatorname {Cov} [Y,X]} represents the covariance operator between random variables X {\displaystyle X} and Y {\displaystyle

Bilinear time–frequency distribution (3,916 words) [view diff] exact match in snippet view article find links to article

) = E [ X ( t ) X ( s ) ] {\displaystyle R(t,s)=E[X(t)X(s)]} The covariance operator K is defined for any deterministic signal f ∈ L 2 ( R ) {\displaystyle

Normal distribution (22,698 words) [view diff] exact match in snippet view article find links to article

Gaussian random element can be described in terms of the linear covariance operator K: H → H. Several Gaussian processes became popular enough to have

Probabilistic numerics (4,269 words) [view diff] exact match in snippet view article find links to article

priors emerge as optimal mixed strategies for such games, and the covariance operator of the optimal Gaussian prior is determined by the quadratic norm

Distributional data analysis (4,575 words) [view diff] exact match in snippet view article find links to article

random element in G {\displaystyle \mathbb {G} } with mean 0 and covariance operator T {\displaystyle {\mathcal {T}}} . Let the eigenvalue-eigenfunction