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In nuclear physics, random matrices were introduced by Eugene Wigner to model the nuclei of heavy atoms. [1] [2] Wigner postulated that the spacings between the lines in the spectrum of a heavy atom nucleus should resemble the spacings between the eigenvalues of a random matrix, and should depend only on the symmetry class of the underlying evolution. [4]
Let X be a p × p symmetric matrix of random variables that is positive semi-definite. Let V be a (fixed) symmetric positive definite matrix of size p × p. Then, if n ≥ p, X has a Wishart distribution with n degrees of freedom if it has the probability density function
Any square matrix can uniquely be written as sum of a symmetric and a skew-symmetric matrix. This decomposition is known as the Toeplitz decomposition. Let Mat n {\displaystyle {\mbox{Mat}}_{n}} denote the space of n × n {\displaystyle n\times n} matrices.
Generator matrix: In Coding theory, a matrix whose rows span a linear code: Gramian matrix: The symmetric matrix of the pairwise inner products of a set of vectors in an inner product space: Hessian matrix: The square matrix of second partial derivatives of a function of several variables: Householder matrix
Matrix pencils play an important role in numerical linear algebra.The problem of finding the eigenvalues of a pencil is called the generalized eigenvalue problem.The most popular algorithm for this task is the QZ algorithm, which is an implicit version of the QR algorithm to solve the eigenvalue problem = without inverting the matrix (which is impossible when is singular, or numerically ...
Random vectors with components sampled independently from the Rademacher distribution are useful for various stochastic approximations, for example: The Hutchinson trace estimator , [ 11 ] which can be used to efficiently approximate the trace of a matrix of which the elements are not directly accessible, but rather implicitly defined via ...
Central normal complex random vectors that are circularly symmetric are of particular interest because they are fully specified by the covariance matrix . The circularly-symmetric (central) complex normal distribution corresponds to the case of zero mean and zero relation matrix, i.e. μ = 0 {\displaystyle \mu =0} and C = 0 {\displaystyle C=0} .
In statistics, the normal distribution is used in classical multivariate analysis, while elliptical distributions are used in generalized multivariate analysis, for the study of symmetric distributions with tails that are heavy, like the multivariate t-distribution, or light (in comparison with the normal distribution). Some statistical methods ...