both.

High values of $\beta$ have also been derived from applying a maximum-likelihood technique to the peculiar velocity data to derive the power-spectrum of mass density fluctuations. These results are summarized in Figure \[fig:ps\]. In the left panel the PS obtained from the ENEAR sample[@wfenear] with those measured for Mark III[@zaroubips] and SFI[@freudling]. The right panel shows the contour map of the likelihood (in the $\Gamma-\eta_8$ plane) for a $\Gamma$ model fit to the ENEAR data, where $\eta_8=\sigma_8\Omega^{0.6}$ and $\sigma_8$ is the $rms$ fluctuation amplitude within a sphere of $8~h^{-1}$ Mpc radius. It is clear from the figure that all data sets lead to similar high-amplitude PS, equivalent to high values of $\beta$. From the figure one also can see that while the likelihood analysis poses a strong constraint on $\eta_8$, the value of $\Gamma$ is poorly determined. Note, however, that low values of $\Gamma$, such as those required from the analyses of redshift survey data ($\Gamma \sim0.2$), are excluded at about the $3\sigma$ level.

It is important to recall that the likelihood method used in estimating the PS involves the use of model power-spectra to compute the velocity correlation tensor which is then compared to that computed from the 