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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.

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