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The covariance matrix is <br> <math>C_{ij} = <(x_i-\bar{x_i})(x_j-\bar{x_j})></math>.<br> Suppose <math>\delta_1,\ \delta_2,\ \delta_3</math> are three independent sources of normally-distributed unit fluctuations (with <span class="texhtml"> &lt; δ<sub>''i''</sub> &gt;  = 0</span> and <span class="texhtml"> &lt; δ<sub>''i''</sub> * δ<sub>''j''</sub> &gt;  = δ<sub>''i''''j'''</sub></span>'''
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The covariance matrix is <br> <math>C_{ij} = <(x_i-\bar{x_i})(x_j-\bar{x_j})></math>.<br> Suppose <math>\delta_1,\ \delta_2,\ \delta_3</math> are three independent sources of normally-distributed unit fluctuations (with <span class="texhtml"> &lt; δ<sub>''i''</sub> &gt;  = 0</span> and <span class="texhtml"> &lt; δ<sub>''i''</sub> * δ<sub>''j''</sub> &gt;  = δ<sub>ij</sub></span>
 
and where <math>\delta_{ij}</math> is the kronikker delta function (1 for <math>i=j</math> and zero for <math>i\neq j</math>).  
 
and where <math>\delta_{ij}</math> is the kronikker delta function (1 for <math>i=j</math> and zero for <math>i\neq j</math>).  
  
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Expanding the covariance matrix one finds <math>C_{ij}=<x_i*x_j>-\bar{x_i}\bar{x_j}</math>.<br>
 
Expanding the covariance matrix one finds <math>C_{ij}=<x_i*x_j>-\bar{x_i}\bar{x_j}</math>.<br>
 
   
 
   
Using the properties of <math><\delta_i*\delta_j></math> above and <math><x_i>=\bar{x_i}</math>, one finds &lt;x_1*x_2&gt; = x_{10}*x_{20}+\beta*\lambda&lt;/math&gt; so that <span class="texhtml">''C''<sub>12</sub> = ''C''<sub>21</sub> = β * λ</span>. Similar substitution for <math>C_{ij}</math> gives for the diagonal matrix elements  
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Using the properties of <math><\delta_i*\delta_j></math> above and <math><x_i>=\bar{x_i}</math>, one finds <math><x_1*x_2> = x_{10}*x_{20}+\beta*\lambda</math>; so that <span class="texhtml">''C''<sub>12</sub> = ''C''<sub>21</sub> = β * λ</span>. Similar substitution gives for the diagonal matrix elements  
 
<br>
 
<br>
<math>C_{11}=x_{10}^2+\alpha^2+\beta^2-x_{10}^2</math><br> and <br> <math>C_{22}=x_{20}^2+\gamma^2+\lambda^2-x_{10}^2</math><br
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<math>C_{11}=x_{10}^2+\alpha^2+\beta^2-x_{10}^2</math><br> and <br> <math>C_{22}=x_{20}^2+\gamma^2+\lambda^2-x_{20}^2</math>
so that C= '''
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<br>
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so that  
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C=
 
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Nåværende revisjon fra 25. mar. 2011 kl. 23:34

The covariance matrix is
[math]C_{ij} = \lt (x_i-\bar{x_i})(x_j-\bar{x_j})\gt [/math].
Suppose [math]\delta_1,\ \delta_2,\ \delta_3[/math] are three independent sources of normally-distributed unit fluctuations (with < δi > = 0 and < δi * δj > = δij and where [math]\delta_{ij}[/math] is the kronikker delta function (1 for [math]i=j[/math] and zero for [math]i\neq j[/math]).


Pseudo-measurements of (x1,x2) can be generated from the expressions (x1,x2) = (x10 + α * δ1 + β * δ2,x20 + γ * δ3 + λ * δ2).

Expanding the covariance matrix one finds [math]C_{ij}=\lt x_i*x_j\gt -\bar{x_i}\bar{x_j}[/math].

Using the properties of [math]\lt \delta_i*\delta_j\gt [/math] above and [math]\lt x_i\gt =\bar{x_i}[/math], one finds [math]\lt x_1*x_2\gt = x_{10}*x_{20}+\beta*\lambda[/math]; so that C12 = C21 = β * λ. Similar substitution gives for the diagonal matrix elements
[math]C_{11}=x_{10}^2+\alpha^2+\beta^2-x_{10}^2[/math]
and
[math]C_{22}=x_{20}^2+\gamma^2+\lambda^2-x_{20}^2[/math]
so that C=

α2 + β2 β * λ
[math]\beta*\lambda[/math] [math]\gamma^2+\lambda^2[/math]