of the fiducial model, because the fiducial parameters only determine the projection matrix $P$ which is fixed throughout. If the initial choice of the fiducial model is far from the correct model, the projection matrix is not the optimal one, and therefore we obtain rather broader profile of the likelihood function around the maximum, because of picking low-S/N modes. One can iterate the likelihood maximization by choosing a fiducial model from the preceding estimation to acquire the narrower profile of the likelihood function. This is the procedure we apply in practical situations.

The model correlation matrix $C({{\mit \Theta}})$ of a reduced data set should be repeatedly calculated for various models, fixing the projection matrix $P$. One still needs to construct the $N\times N$ matrix $R({{\mit \Theta}})$ as many times as $C$, because $C({{\mit \Theta}}) = P R({{\mit \Theta}})
P^{\rm T}$. It is only when this process is omitted that the parameter dependence on $R$ is linear or polynomial. However, most of the important parameters are not linearly or polynomially dependent on the correlation matrix. On that ground, a good modeling and numerically fast construction of the correlation matrix $R$ is essential.

 Bayesian Analysis of the Galaxy Distribution in Redshift Surveys 