  We describe the Bayesian-based signal-to-noise eigenmode method for
cosmological parameter estimation, show how it can be used to optimally
compress large CMB anisotropy data sets to manageable sizes, and apply it to
the DMR 4-year, South Pole and Saskatchewan data, individually and in
combination. A simple prior probability method is used to include large scale
structure observations. Estimates of the Hubble parameter, vacuum energy
density, baryon fraction and primordial spectral tilt derived from the combined
data are given.
