  Markov Chain Monte Carlo (MCMC) sampler is widely used for cosmological
parameter estimation from CMB and other data. However, due to the intrinsic
serial nature of the MCMC sampler, convergence is often very slow. Here we
present a fast and independently written Monte Carlo method for cosmological
parameter estimation named as Slick Cosmological Parameter Estimator (SCoPE),
that employs delayed rejection to increase the acceptance rate of a chain, and
pre-fetching that helps an individual chain to run on parallel CPUs. An
inter-chain covariance update is also incorporated to prevent clustering of the
chains allowing faster and better mixing of the chains. We use an adaptive
method for covariance calculation to calculate and update the covariance
automatically as the chains progress. Our analysis shows that the acceptance
probability of each step in SCoPE is more than $95\%$ and the convergence of
the chains are faster. Using SCoPE, we carry out some cosmological parameter
estimations with different cosmological models using WMAP-9 and Planck results.
One of the current research interests in cosmology is quantifying the nature of
dark energy. We analyze the cosmological parameters from two illustrative
commonly used parameterisations of dark energy models. We also asses primordial
helium fraction in the universe can be constrained by the present CMB data from
WMAP-9 and Planck. The results from our MCMC analysis on the one hand helps us
to understand the workability of the SCoPE better, on the other hand it
provides a completely independent estimation of cosmological parameters from
WMAP-9 and Planck data.
