Karhunen-Loeve theorem - определение. Что такое Karhunen-Loeve theorem
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Что (кто) такое Karhunen-Loeve theorem - определение

THEORY OF STOCHASTIC PROCESSES
Karhunen-Loève transform; Karhunen-Loeve transform; Karhunen-Loeve theorem; Karhunen-loève expansion; Karhunen-Loève decomposition; Karhunen-Loeve decomposition; Karhunen-loeve expansion; Karhunen-Loève theorem; Karhunen–Loeve expansion; Karhunen–Loève expansion; Karhunen-Loeve expansion; Karhunen–Loeve transform; Karhunen–Loève transform; Karhunen-Loève expansion; Karhunen–Loeve theorem; Loève–Karhunen theorem; Loève–Karhunen theoem; Loève-Karhunen theorem; Loève-Karhunen theoem; Karhunen–Loève theorem; Kosambi-Karhunen-Loève theorem

Kosambi–Karhunen–Loève theorem         
In the theory of stochastic processes, the Karhunen–Loève theorem (named after Kari Karhunen and Michel Loève), also known as the Kosambi–Karhunen–Loève theorem is a representation of a stochastic process as an infinite linear combination of orthogonal functions, analogous to a Fourier series representation of a function on a bounded interval. The transformation is also known as Hotelling transform and eigenvector transform, and is closely related to principal component analysis (PCA) technique widely used in image processing and in data analysis in many fields.
Loève Prize         
AWARD
Loeve prize; Loeve Prize
The Line and Michel Loève International Prize in Probability (Loève Prize) was created in 1992 in honor of Michel Loève by his widow Line. The prize, awarded every two years, is intended to recognize outstanding contributions by researchers in mathematical probability who are under 45 years old.
Divergence theorem         
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  • A volume divided into two subvolumes. At right the two subvolumes are separated to show the flux out of the different surfaces.
  • The volume can be divided into any number of subvolumes and the flux out of ''V'' is equal to the sum of the flux out of each subvolume, because the flux through the <span style="color:green;">green</span> surfaces cancels out in the sum. In (b) the volumes are shown separated slightly, illustrating that each green partition is part of the boundary of two adjacent volumes
  • </math> approaches <math>\operatorname{div} \mathbf{F}</math>
  • The divergence theorem can be used to calculate a flux through a [[closed surface]] that fully encloses a volume, like any of the surfaces on the left. It can ''not'' directly be used to calculate the flux through surfaces with boundaries, like those on the right. (Surfaces are blue, boundaries are red.)
  • The vector field corresponding to the example shown. Vectors may point into or out of the sphere.
GENERALIZATION OF THE FUNDAMENTAL THEOREM IN VECTOR CALCULUS
Gauss' theorem; Gauss's theorem; Gauss theorem; Ostrogradsky-Gauss theorem; Ostrogradsky's theorem; Gauss's Theorem; Divergence Theorem; Gauss' divergence theorem; Ostrogradsky theorem; Gauss-Ostrogradsky theorem; Gauss Ostrogradsky theorem; Gauss–Ostrogradsky theorem
In vector calculus, the divergence theorem, also known as Gauss's theorem or Ostrogradsky's theorem, reprinted in is a theorem which relates the flux of a vector field through a closed surface to the divergence of the field in the volume enclosed.

Википедия

Kosambi–Karhunen–Loève theorem

In the theory of stochastic processes, the Karhunen–Loève theorem (named after Kari Karhunen and Michel Loève), also known as the Kosambi–Karhunen–Loève theorem states that a stochastic process can be represented as an infinite linear combination of orthogonal functions, analogous to a Fourier series representation of a function on a bounded interval. The transformation is also known as Hotelling transform and eigenvector transform, and is closely related to principal component analysis (PCA) technique widely used in image processing and in data analysis in many fields.

Stochastic processes given by infinite series of this form were first considered by Damodar Dharmananda Kosambi. There exist many such expansions of a stochastic process: if the process is indexed over [a, b], any orthonormal basis of L2([a, b]) yields an expansion thereof in that form. The importance of the Karhunen–Loève theorem is that it yields the best such basis in the sense that it minimizes the total mean squared error.

In contrast to a Fourier series where the coefficients are fixed numbers and the expansion basis consists of sinusoidal functions (that is, sine and cosine functions), the coefficients in the Karhunen–Loève theorem are random variables and the expansion basis depends on the process. In fact, the orthogonal basis functions used in this representation are determined by the covariance function of the process. One can think that the Karhunen–Loève transform adapts to the process in order to produce the best possible basis for its expansion.

In the case of a centered stochastic process {Xt}t ∈ [a, b] (centered means E[Xt] = 0 for all t ∈ [a, b]) satisfying a technical continuity condition, X admits a decomposition

X t = k = 1 Z k e k ( t ) {\displaystyle X_{t}=\sum _{k=1}^{\infty }Z_{k}e_{k}(t)}

where Zk are pairwise uncorrelated random variables and the functions ek are continuous real-valued functions on [a, b] that are pairwise orthogonal in L2([a, b]). It is therefore sometimes said that the expansion is bi-orthogonal since the random coefficients Zk are orthogonal in the probability space while the deterministic functions ek are orthogonal in the time domain. The general case of a process Xt that is not centered can be brought back to the case of a centered process by considering XtE[Xt] which is a centered process.

Moreover, if the process is Gaussian, then the random variables Zk are Gaussian and stochastically independent. This result generalizes the Karhunen–Loève transform. An important example of a centered real stochastic process on [0, 1] is the Wiener process; the Karhunen–Loève theorem can be used to provide a canonical orthogonal representation for it. In this case the expansion consists of sinusoidal functions.

The above expansion into uncorrelated random variables is also known as the Karhunen–Loève expansion or Karhunen–Loève decomposition. The empirical version (i.e., with the coefficients computed from a sample) is known as the Karhunen–Loève transform (KLT), principal component analysis, proper orthogonal decomposition (POD), empirical orthogonal functions (a term used in meteorology and geophysics), or the Hotelling transform.