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  2. Matérn covariance function - Wikipedia

    en.wikipedia.org/wiki/Matérn_covariance_function

    In statistics, the Matérn covariance, also called the Matérn kernel, [1] is a covariance function used in spatial statistics, geostatistics, machine learning, image analysis, and other applications of multivariate statistical analysis on metric spaces. It is named after the Swedish forestry statistician Bertil Matérn. [2]

  3. Covariance function - Wikipedia

    en.wikipedia.org/wiki/Covariance_function

    In probability theory and statistics, the covariance function describes how much two random variables change together (their covariance) with varying spatial or temporal separation. For a random field or stochastic process Z ( x ) on a domain D , a covariance function C ( x , y ) gives the covariance of the values of the random field at the two ...

  4. Gradient-enhanced kriging - Wikipedia

    en.wikipedia.org/wiki/Gradient-enhanced_kriging

    The LHS-design is available in scripting codes like MATLAB or Python. Make observations: For each sample in our DoE one runs the computer simulation to obtain the Quantity of Interest (QoI). Construct the surrogate: One uses the GEK predictor equations to construct the surrogate conditional on the obtained observations.

  5. Estimation of covariance matrices - Wikipedia

    en.wikipedia.org/wiki/Estimation_of_covariance...

    The sample covariance matrix (SCM) is an unbiased and efficient estimator of the covariance matrix if the space of covariance matrices is viewed as an extrinsic convex cone in R p×p; however, measured using the intrinsic geometry of positive-definite matrices, the SCM is a biased and inefficient estimator. [1]

  6. Multivariate kernel density estimation - Wikipedia

    en.wikipedia.org/wiki/Multivariate_kernel...

    We consider estimating the density of the Gaussian mixture (4π) −1 exp(− 1 ⁄ 2 (x 1 2 + x 2 2)) + (4π) −1 exp(− 1 ⁄ 2 ((x 1 - 3.5) 2 + x 2 2)), from 500 randomly generated points. We employ the Matlab routine for 2-dimensional data. The routine is an automatic bandwidth selection method specifically designed for a second order ...

  7. Kosambi–Karhunen–Loève theorem - Wikipedia

    en.wikipedia.org/wiki/Kosambi–Karhunen–Loève...

    The covariance function K X satisfies the definition of a Mercer kernel. By Mercer's theorem, there consequently exists a set λ k, e k (t) of eigenvalues and eigenfunctions of T K X forming an orthonormal basis of L 2 ([a,b]), and K X can be expressed as

  8. Kernel principal component analysis - Wikipedia

    en.wikipedia.org/wiki/Kernel_principal_component...

    in other words, it gives an eigendecomposition of the covariance matrix: λ v = C v {\displaystyle \lambda \mathbf {v} =C\mathbf {v} } which can be rewritten as

  9. Joint Probabilistic Data Association Filter - Wikipedia

    en.wikipedia.org/wiki/Joint_Probabilistic_Data...

    The sample code in demo2DDataAssociation demonstrates how the algorithms can be used in a simple scenario. Python: The PDAF, JPDAF and other data association methods are implemented in Stone-Soup. [10] A tutorial demonstrates how the algorithms can be used. [11] [12]