We present a fast Markov Chain Monte-Carlo exploration of cosmological parameter space .
We perform a joint analysis of results from recent CMB experiments and provide parameter constraints , including \sigma _ { 8 } , from the CMB independent of other data .
We next combine data from the CMB , HST Key Project , 2dF galaxy redshift survey , supernovae Ia and big-bang nucleosynthesis .
The Monte Carlo method allows the rapid investigation of a large number of parameters , and we present results from 6 and 9 parameter analyses of flat models , and an 11 parameter analysis of non-flat models .
Our results include constraints on the neutrino mass ( m _ { \nu } \lesssim 0.3 \text { eV } ) , equation of state of the dark energy , and the tensor amplitude , as well as demonstrating the effect of additional parameters on the base parameter constraints .
In a series of appendices we describe the many uses of importance sampling , including computing results from new data and accuracy correction of results generated from an approximate method .
We also discuss the different ways of converting parameter samples to parameter constraints , the effect of the prior , assess the goodness of fit and consistency , and describe the use of analytic marginalization over normalization parameters .