Recursive updating the eigenvalue decomposition of a covariance matrix

Posted by / 10-Oct-2016 08:46

Recursive updating the eigenvalue decomposition of a covariance matrix

In mathematics, a linear combination is an expression constructed from a set of terms by multiplying each term by a constant and adding the results (e.g.

a linear combination of x and y would be any expression of the form ax by, where a and b are constants).

A dictionary (also called a wordbook, lexicon or vocabulary) is a collection of words in one or more specific languages, often listed alphabetically, with usage information, definitions, etymologies, phonetics, pronunciations, and other information; or a book of words in one language with their equivalents in another, also known as a lexicon.

In statistics, a data point is a set of measurements on a single member of a statistical population, or a subset of those measurements for a given individual.

Traditionally, neuroscience has been seen as a branch of biology.

An auxiliary regression is run in which the dependent variable is the residual from the last model and the independent variables are those from the last model plus varlist.

Performs a joint test for the addition of the specified variables to the last model, the results of which may be retrieved using the accessors $test and $pvalue.

By default an augmented version of the original model is estimated, including the variables in varlist.

The Gaussian Process Round Table meeting in Sheffield, June 9-10, 2005. The Bibliography of Gaussian Process Models in Dynamic Systems Modelling web site maintained by Juš Kocijan.

Andreas Geiger has written a simple Gaussian process regression Java applet, illustrating the behaviour of covariance functions and hyperparameters.

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These manifolds represent the constraints that arise in such areas as the symmetric eigenvalue problem, nonlinear eigenvalue problems, electronic structures computations, and signal proces ..." In this paper we develop new Newton and conjugate gradient algorithms on the Grassmann and Stiefel manifolds.

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