Covariance

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

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

CXCOV Circular Cross Covariance function estimates.CXCOV(a,b), where a and b represent samples taken over time interval T, which is assumed to be a common period of two corresponding periodic signals.a and b are supposed to be length M row vectors, either real or complex. [x,c]=CXCOV(a,b) returns...


Platforms: Matlab

Y = DOWNSIDECOVARIANCE(Y) returns the downside covariance for columns of variable Y.Y = DOWNSIDECOVARIANCE(Y, m) returns the downside covariance using vector m as expected value for columns of variable Y.==================================================Downside covariance is defined as E[min(yi...


Platforms: Matlab

Y = UPSIDECOVARIANCE(Y) returns the upside covariance for columns ofvariable Y.Y = UPSIDECOVARIANCE(Y, m) returns the upside covariance using vector mas expected value for columns of variable Y.====================================================Upside covariance is defined as E[max(yi - mi, 0)...


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

Covariance NMR toolbox, version 1.1 (C) (2010)David A. Snyder(1) along with Timothy Short(1), Leigh Alzapiedi(1) and Rafael Bruschweiler (2)(1) Department of Chemistry, College of Science and Health, William Paterson University; (2) Department of Chemistry and NHMFL, Florida State UniversityThe...


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

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

- Allows for >=2 classes- Requires only base MATLAB (no toolboxes needed)- Assumes that the data is complete (no missing values)- Has been verified against statistical softwareWarning:- if pooled covariance could not be calculated matrix values will be ("Inf") (no error)-requires Matlab >=7.6...


Platforms: Matlab

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

Statistics is a well tested element, which provides some basic statistical calculations, such as average, variance, covariance and more. In addition it provides the values of the Standard Normal Distribution and of the T Distribution. The element has one basic procedure that concerns the element...


Platforms: Windows

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

RiskMetrics.m: Estimates the univariate or multivariate RiskMetrics. USAGE: rm = RiskMetrics(data,alpha) INPUTS: data = ( m x n ) vector lamba = the scale parameter method = Univariate or Multivariate OUTPUTS: rm = ( m x n ) volatility vector for the univariate case or an [( n x n )x m]...


Platforms: Matlab

Free Split and Merge Expectation-Maximization algorithm for Multivariate Gaussian Mixtures. This algorithm is suitable to estimate mixture parameters and the number of conpounds Usage ------- [logl , M , S , P] = fsmem_mvgm(Z , [M0] , [S0] , [P0] , [option]); Inputs ------- Z Measurements (d x N)...


Platforms: Matlab

Value-at-Risk calculation for portfolio stocks using variance-covariance, historical and MonteCarlo methods. Portfolio can be larger as you want including either the risk factor (stock index, currency, etc.)


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

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