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・ Covenant Christian High School (Michigan)
・ Covenant Christian School
・ Covenant Christian School (Canberra)
・ Covaresa
・ Covariance
・ Covariance (disambiguation)
・ Covariance and contravariance
・ Covariance and contravariance (computer science)
・ Covariance and contravariance of vectors
・ Covariance and correlation
・ Covariance function
・ Covariance group
・ Covariance intersection
・ Covariance mapping
・ Covariance matrix
Covariance operator
・ Covariant classical field theory
・ Covariant derivative
・ Covariant formulation of classical electromagnetism
・ Covariant Hamiltonian field theory
・ Covariant return type
・ Covariant transformation
・ Covariate
・ Covariation model
・ Covario
・ Covarion
・ Covarrubias
・ Covarrubias (surname)
・ Covarrubias, Province of Burgos
・ Covas


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Covariance operator : ウィキペディア英語版
Covariance operator
In probability theory, for a probability measure P on a Hilbert space ''H'' with inner product \langle \cdot,\cdot\rangle , the covariance of P is the bilinear form Cov: ''H'' × ''H'' → R given by
:\mathrm(x, y) = \int_ \langle x, z \rangle \langle y, z \rangle \, \mathrm \mathbf (z)
for all ''x'' and ''y'' in ''H''. The covariance operator ''C'' is then defined by
:\mathrm(x, y) = \langle Cx, y \rangle
(from the Riesz representation theorem, such operator exists if Cov is bounded). Since Cov is symmetric in its arguments, the covariance operator is
self-adjoint (the infinite-dimensional analogy of the transposition symmetry in the finite-dimensional case). When P is a centred Gaussian measure, ''C'' is also a nuclear operator. In particular, it is a compact operator of trace class, that is, it has finite trace.
Even more generally, for a probability measure P on a Banach space ''B'', the covariance of P is the bilinear form on the algebraic dual ''B''#, defined by
:\mathrm(x, y) = \int_ \langle x, z \rangle \langle y, z \rangle \, \mathrm \mathbf (z)
where \langle x, z \rangle is now the value of the linear functional ''x'' on the element ''z''.
Quite similarly, the covariance function of a function-valued random element (in special cases called random process or random field) ''z'' is
:\mathrm(x, y) = \int z(x) z(y) \, \mathrm \mathbf (z) = E(z(x) z(y))
where ''z''(''x'') is now the value of the function ''z'' at the point ''x'', i.e., the value of the linear functional u \mapsto u(x) evaluated at ''z''.



抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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