of the Bayesian approach. One needs to repeatedly evaluate this factor to maximize the posterior probability in multi-dimensional parameter space. Therefore, the numerator should be numerically calculated as fast as possible. This part of the calculation is usually not easy, especially when the the data $D$ consists of a large set of observational figures. In galaxy redshift surveys, the data $D$ consists of the distribution of galaxies in redshift space. These days the scales of redshift surveys become more and more immense. In the 2dF Galaxy Redshift Survey [@col01], redshifts of about 220,000 galaxies are observed in the sky coverage of 2,000 square degrees. In the Sloan Digital Sky Survey (SDSS) [@yor00], redshifts of about 1,000,000 galaxies are planned to be observed over 10,000 square degrees of the sky.

When the prior information on the model, $I$, has nothing to do with obtaining the data themselves, the numerator of equation (\[eq2-1\]) is equal to $P(D|{{\mit \Theta}})$. In addition, when the statistical distribution of the data $D$ is given by the multivariate Gaussian distribution, this factor has the form, $$P(D|{{\mit \Theta}}) =
   \frac{1}{\sqrt{(2\pi)^N \det R({{\mit \Theta}})}}
   \exp\left[ -\frac12
     \left({{\mbox{\boldmath $d$}}}^{\rm T} - \langle{{\mbox{\boldmath $d$}}}^{\rm T}\rangle_{{{\mit \Theta}}}\right)
       R^{-1}({{\mit \Theta}}) \left({{\mbox{\boldmath $d$}}} -
   