Revisiting Cosmological parameter estimation
  Constraining theoretical models with measuring the parameters of those from
cosmic microwave background (CMB) anisotropy data is one of the most active
areas in cosmology. WMAP, Planck and other recent experiments have shown that
the six parameters standard $\Lambda$CDM cosmological model still best fits the
data. Bayesian methods based on Markov-Chain Monte Carlo (MCMC) sampling have
been playing leading role in parameter estimation from CMB data. In one of the
recent studies \cite{2012PhRvD..85l3008P} we have shown that particle swarm
optimization (PSO) which is a population based search procedure can also be
effectively used to find the cosmological parameters which are best fit to the
WMAP seven year data. In the present work we show that PSO not only can find
the best-fit point, it can also sample the parameter space quite effectively,
to the extent that we can use the same analysis pipeline to process PSO sampled
points which is used to process the points sampled by Markov Chains, and get
consistent results. We also present implementations of downhill-simplex Method
of Nelder and Mead and Powell's method of Bound Optimization BY Quadratic
Approximation (BOBYQA) in this work for cosmological parameter estimation, and
compare these methods with PSO. Since PSO has the advantage that it only needs
the search range and does not need covariance-matrix, starting point or any
other quantity which depend on the final results, it can be quite useful for a
blind search of the best fit parameters. Apart from that, PSO is based on a
completely different algorithm so it can supplement MCMC methods. We use PSO to
estimate parameters from the WMAP nine year and Planck data and get consistent
results.
