A fast test to assess the impact of marginalization in Monte Carlo analyses, and its application to cosmology

Understand

Monte Carlo (MC) algorithms are commonly employed to explore high-dimensional parameter spaces constrained by data.

• All the statistical information obtained in the output of these analyses is contained in the Markov chains, which one needs to process and interpret.
• The marginalization technique allows us to digest these chains and compute the posterior distributions for the parameter subsets of interest.
• In particular, it lets us draw confidence regions in two-dimensional planes, and get the constraints for the individual parameters.