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A matrix (plural matrices, or less commonly matrixes) is a rectangular array of numbers called entries. Matrices have a long history of both study and application, leading to diverse ways of classifying matrices. A first group is matrices satisfying concrete conditions of the entries, including constant matrices.
Throughout this article, boldfaced unsubscripted and are used to refer to random vectors, and Roman subscripted and are used to refer to scalar random variables.. If the entries in the column vector = (,, …,) are random variables, each with finite variance and expected value, then the covariance matrix is the matrix whose (,) entry is the covariance [1]: 177 ...
The Wigner semicircle distribution is important in the theory of random matrices. The continuous Bernoulli distribution is a one-parameter exponential family that provides a probabilistic counterpart to the binary cross-entropy loss. Chi-squared distribution Gamma distribution Pareto distribution
Pages in category "Statistical deviation and dispersion" The following 84 pages are in this category, out of 84 total. This list may not reflect recent changes .
Suppose G is a p × n matrix, each column of which is independently drawn from a p-variate normal distribution with zero mean: = (, …,) (,). Then the Wishart distribution is the probability distribution of the p × p random matrix [4]
For several parameters, the covariance matrices and information matrices are elements of the convex cone of nonnegative-definite symmetric matrices in a partially ordered vector space, under the Loewner (Löwner) order. This cone is closed under matrix addition and inversion, as well as under the multiplication of positive real numbers and ...
— Fibonacci matrix — Fibonacci Q-matrix — Fourier matrix — Frobenius matrix — Game matrix (same as payoff matrix?) — Gordon matrix — H-matrix — Conference matrix — Dispersion matrix (same as covariance matrix) — Reciprocal matrix - A=(a ij) with a ji =1/a ij. Mentioned by Saaty, TL, Eur J Operation Research 48(1990) 9 ...
^ = = (¯) (¯) = [′ ()] (matrix form; is the identity matrix, J is a matrix of ones; the term in parentheses is thus the centering matrix) The Fisher information matrix for estimating the parameters of a multivariate normal distribution has a closed form expression.