@@ -374,26 +374,26 @@ Next, we move to the basis in which :math:`\rho` is diagonal. Writing :math:`\rh
374374 \chi ^2 &= \tilde {\epsilon }^T \rho ^{-1 } \tilde {\epsilon } \\
375375 &= \tilde {\epsilon }^T (\tilde {U}^T \tilde {\Lambda } \tilde {U})^{-1 } \tilde {\epsilon } \\
376376 &= \tilde {\epsilon }^T \tilde {U}^T \tilde {\Lambda }^{-1 } \tilde {U} \tilde {\epsilon } \\
377- &\equiv \dbtilde { \ epsilon }^T \tilde {\Lambda }^{-1 } \dbtilde { \ epsilon } \, ,
377+ &\equiv \tilde { \tilde { \ epsilon }} ^T \tilde {\Lambda }^{-1 } \tilde { \tilde { \ epsilon} } \, ,
378378
379379 where on the last line we have defined
380380
381381.. math ::
382382
383- \dbtilde { \ epsilon } \equiv \tilde {U}\tilde {\epsilon } = \tilde {U}R^{-1 }(D-T).
383+ \tilde { \tilde { \ epsilon} } \equiv \tilde {U}\tilde {\epsilon } = \tilde {U}R^{-1 }(D-T).
384384
385385 In index notation, this reads
386386
387387.. math ::
388388
389- \dbtilde { \ epsilon _i} = \tilde {U}_{ij} \frac {(D-T)_j}{\sqrt {C_{0 , jj}}}
389+ \tilde { \tilde { \ epsilon _i} } = \tilde {U}_{ij} \frac {(D-T)_j}{\sqrt {C_{0 , jj}}}
390390
391- The transformed data :math: `\dbtilde { \ epsilon }` is statistically independent in the diagonal basis of the correlation matrix :math: `\rho `.
392- Computing the covariance of :math: `\dbtilde { \ epsilon }`,
391+ The transformed data :math: `\tilde { \tilde { \ epsilon} }` is statistically independent in the diagonal basis of the correlation matrix :math: `\rho `.
392+ Computing the covariance of :math: `\tilde { \tilde { \ epsilon} }`,
393393
394394.. math ::
395395
396- \mathbb {E}[\dbtilde { \ epsilon }\dbtilde { \ epsilon }^T]
396+ \mathbb {E}[\tilde { \tilde { \ epsilon }} \tilde { \tilde { \ epsilon} }^T]
397397 &= \mathbb {E} \big [ (\tilde {U} R^{-1 }(D-T)) (\tilde {U} R^{-1 }(D-T))^T \big ] \\
398398 &= \tilde {U} R^{-1 } \mathbb {E}[(D-T)(D-T)^T] R^{-1 } \tilde {U}^T \\
399399 &= \tilde {U} \rho \tilde {U}^T \\
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