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annotate m-toolbox/classes/+utils/@math/corr2cov.m @ 22:b11e88004fca database-connection-manager

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author Daniele Nicolodi <nicolodi@science.unitn.it>
date Mon, 05 Dec 2011 16:20:06 +0100
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1 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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Daniele Nicolodi <nicolodi@science.unitn.it>
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2 % CORR2COV
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3 % Convert correlartion matrix to covariance matrix
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4 %
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5 % SigC Vector of length n with the standard deviations of each process. n
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6 % is the number of random processes.
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7 %
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8 % CorrC n-by-n correlation coefficient matrix. If ExpCorrC is not
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9 % specified, the processes are assumed to be uncorrelated, and the identity
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10 % matrix is used.
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11 %
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12 % Algorithm
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13 %
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14 % Covar(i,j) = CorrC(i,j)*(SigmC(i)*SigmC(j)
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15 %
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16 % L Ferraioli 10-10-2010
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17 %
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18 % $Id: corr2cov.m,v 1.1 2010/11/16 16:41:37 luigi Exp $
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19 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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20
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21
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22 function Covar = corr2cov(CorrC,SigC)
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23
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24 Covar = CorrC;
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25
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26 for tt=1:size(CorrC,1)
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27 for hh=1:size(CorrC,2)
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28 Covar(tt,hh) = CorrC(tt,hh)*SigC(tt)*SigC(hh);
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29 end
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31 end