the number counts $d_i$ in a cell $i$ is the data $D$ in redshift surveys. The pixel size determines the resolution in the analysis, so it is desirable to adopt as many cells in redshift space as possible. However, too many cells cause trouble in calculating the inverse of the correlation matrix in equation (\[eq2-2\]). The caluculation of the inverse of the huge matrix requires a lot of computation time, which is primarily an $N^3$ process and can be an $N^{2.8}$ process at most [@pre92]. This is the bottle neck in the Bayesian analysis since the inversion should be repeatedly performed for every set of parameters.

The pixelized raw data vector ${{\mbox{\boldmath $d$}}} = (d_1, d_2, \ldots, d_N)^{\rm
T}$ consists of clustering signals and noises. Most of the process in inverting the correlation matrix in the above procedure might be devoted to dealing with noises. Since the information on the cosmological model is only contained in the signals, it is beneficial to reduce the dimension of the correlation matrix, keeping the maximal information on the clustering signals and minimizing contributions by the noise. One simple way to achieve this aim is to consider a linear projection of the raw data 