$LastChangedDate: 27-Juil-2011 $

# CL_stat

Statistics on a matrix of samples

## Calling Sequence

       [means,cov] = CL_stat(samples)

## Description

- Computes the mean and the covariance matrix from samples.

## Parameters

samples:

  Matrix of sample values. Each column vector is a different sample. Each sample contains the values of P (random) variables. (PxN)

means:

  Estimated mean values (Px1)

cov:

  Estimated covariance matrix (PxP)

## Authors

- CNES - DCT/SB

## See also

- CL_cor2cov

## Examples

    // given correlation in cartesian orbital elements,
    // compute correlation in adapted circular
    // orbital elements with Monte Carlo process
    bul = [-1877901 -3909428 -5026025 7428.157 -1541.857 -1576.532]';
    cor = [1 -0.467016 -0.447601 0.960396 0.987145 0.995826;...
    0 1 -0.088751 -0.359696 -0.412472 -0.540655;...
    0 0 1 -0.248472 -0.582834 -0.431908;...
    0 0 0 1 0.915197 0.943178;...
    0 0 0 0 1 0.980679;...
    0 0 0 0 0 1]; //upper triangle of correlation matrix
    cor = cor+cor'-eye(cor); // correlation matrix (symetrical)
    // standard deviations
    sd = [15939.68154 2912.099353 3079.494708 6.81910416 9.50017639 12.14624495]';
    cov = CL_cor2cov(cor,sd);
    // Draw 10000 samples following correlation :
    drawn_car = CL_covDraw(bul,cov,10000);
    // Convert samples to adapted circular :
    drawn_cir = CL_oe_car2cir(drawn_car(1:3,:),drawn_car(4:6,:));
    [mean_cir,cov_cir] = CL_stat(drawn_cir);