  The interpretation of cosmological observables requires the use of
increasingly sophisticated theoretical models. Since these models are becoming
computationally very expensive and display non-trivial uncertainties, the use
of standard Bayesian algorithms for cosmological inferences, such as MCMC,
might become inadequate. Here, we propose a new approach to parameter
estimation based on an iterative Gaussian emulation of the target likelihood
function. This requires a minimal number of likelihood evaluations and
naturally accommodates for stochasticity in theoretical models. We apply the
algorithm to estimate 9 parameters from the monopole and quadrupole of a mock
power spectrum in redshift space. We obtain accurate posterior distribution
functions with approximately 100 times fewer likelihood evaluations than an
affine invariant MCMC, roughly independently from the dimensionality of the
problem. We anticipate that our parameter estimation algorithm will accelerate
the adoption of more accurate theoretical models in data analysis, enabling
more comprehensive exploitation of cosmological observables.
