Covariance operator
In probability theory, for a probability measure P on a Hilbert space H with inner product , the covariance of P is the bilinear form Cov: H × H → R given by
for all x and y in H. The covariance operator C is then defined by
(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.
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
where 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 is called random process or random field) z is
where z(x) is now the value of the function z at the point x, i.e., the value of the linear functional evaluated at z.
See also
[edit]- Abstract Wiener space – Mathematical construction relating to infinite-dimensional spaces.
- Cameron–Martin theorem – Theorem defining translation of Gaussian measures (Wiener measures) on Hilbert spaces.
- Feldman–Hájek theorem – Theory in probability theory
- Structure theorem for Gaussian measures – Mathematical theorem
Further reading
[edit]- Baker, C. R. (September 1970). On Covariance Operators. Mimeo Series. Vol. 712. University of North Carolina at Chapel Hill.
- Baker, C. R. (December 1973). "Joint Measures and Cross-Covariance Operators" (PDF). Transactions of the American Mathematical Society. 186: 273–289.
- Vakhania, N. N.; Tarieladze, V. I.; Chobanyan, S. A. (1987). "Covariance Operators". Probability Distributions on Banach Spaces. Dordrecht: Springer Netherlands. pp. 144–183. doi:10.1007/978-94-009-3873-1_3. ISBN 978-94-010-8222-8. Retrieved 2024-04-11.
References
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