Covariance Matrix

Return covariance matrix for given data matrix X (row = object, column = feature)% @author: Kardi Teknomo% http://people.revoledu.com/kardi/index.htmlSee the usage for Mahalanobis distance computation in http://people.revoledu.com/kardi/tutorial/...isDistance.html Platforms: Matlab

It will compute the Kalman gain and the stationary covariance matrix using a Kalman filter with a linear forward looking model. Requirements: - MATLAB 7 or higher Platforms: Windows, Mac, *nix, Matlab, BSD Solaris

Calculates the RiskMetrics "Technical Document" (1996) exponentially weighted covariance matrix (p.179), correlation and volatilities.Input:data - needs to be in format T x k with T = # observations, k = # assetslambda = decay factor Platforms: Matlab

MVNRND2 Random vectors from the multivariate normal distribution. R = MVNRND2(MU,SIGMA,NUM) returns a NUM-by-D matrix R of multivariate normal random vectors whose mean and covariance matrix match the given input parameters, MU (1-D vector) and SIGMA (D-by-D matrix) [...] = MVNRND2(...,COVNORM)... Platforms: Matlab

Revised public version of covariance matrix tools. (see attached Contents file). Platforms: Matlab

The function is "remix" of native matlab cov2corr() function, which produces correlation matrix with elements on its main diagonal slightly greater or less then 1. So it can't be used in various further computations, for example in squareform() function.The problem can be resolved simply by... Platforms: Matlab

Using the Cholesky decomposition, it generates n iterations of multivariate Gaussian random variables for a given mean vector (mu) and variance-covariance matrix (sigma).mvgrnd(mu,sigma,n) Platforms: Matlab

The function performs a nonlinear, constrained optimization to find a positive semi-definite matrix that is closest (2-norm) to a symmetric matrix that is not positive semi-definite which the user provides to the function. The optimization is subject to the constraint that the output matrix'... Platforms: Matlab

R = RCR(S) computes the semi-edge-length of the mean-centered hypercube with 95% probability given S, which is either a covariance matrix or a vector of standard deviations from a multivariate normal distribution. If S is a real, nonnegative vector, RCR(S) is equivalent to RCR(DIAG(S.^2)). Scalar... Platforms: Matlab

Computes the variance of a linear sum of variables. Requires a vector of weights (for the linear sum), and a covariance matrix of the variables. Platforms: Matlab

I faced a stupid problem of getting all possible orders of variables for Cholesky decomposition of a covariance matrix. Web search offered advertisements of 60-line codes as a solution, so I decided to crack it myself. Enjoy. Platforms: Matlab

Fits the Laird-Ware Linear Random Effects Model. This model assumes that for each subject y=x*b+z*g+e where x and z are known m x p and m x r matricies, b is p x 1 parameter vector and g is a r vector which has a multivariate normal distribution with mean zero, e is a vector of identitcal... Platforms: Matlab

It will compute the Kalman gain and the stationary covariance matrix using a Kalman filter with a linear forward looking model. Platforms: Matlab

Function that transforms a non positive definite symmetric matrix to a positive definite.Function that transforms a non positive definite symmetric matrix to positive definite symmetric matrix -i.e. invertible-.One particular case could be the inversion of a covariance matrix. The... Platforms: Matlab

The application uses the approach introduced in paper "Covariance Tracking using Model Update Based on Means on Riemannian Manifolds", F.Porikli, O.Tuzel, P.Meer.The tracking is based on:1) initializing the target region2) constructing the Feature Vectors for each pixel in the target region... Platforms: Matlab

We can exploit the symmetry of a real, positive definite, symmetric matrix by using the Cholesky decomposition to compute the inverse.The built-in MATLAB inv function uses LU decomposition which requires extra pivots and operations. Rarely does one actually need to compute the inverse of a matrix... Platforms: Matlab

Let us assume the following MIMO model:Y(k)=HX(k)+Y(k)This file can detect the number of sources, i.e the size of the vector X(k) from the received signal Y(k). The noise is assumed spatially white and the number of receivers must be strictly greater than the number of sources. For more details... Platforms: Matlab

1. Detailed Tutorial on Kalman Filtering Techniques in Matlab2.Computes the Kalman gain and the stationary covariance matrix using the Kalman filter of a linear forward looking model. Platforms: Matlab

Mex implementation of EM algorithm for multivariate Gaussian mixture. Multiple data/initial parameters are allowed by ND slices definition em_mvgm : Expectation-Maximization algorithm for Multivariate Gaussian Mixtures Usage ------- [logl , M , S , P] = em_mvgm(Z , M0 , S0 , P0 , [nbite]); Inputs... Platforms: Matlab

OptiPt is a function for fitting and testing (multi-attribute) probabilistic choice models, especially the Bradley-Terry-Luce (BTL) model (Bradley & Terry, 1952; Luce, 1959), elimination-by-aspects (EBA) models (Tversky, 1972), and preference tree (Pretree) models (Tversky & Sattath,... Platforms: Matlab