English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Monte Carlo (MC) algorithms are commonly employed to explore high-dimensionalparameter spaces constrained by data. All the statistical information obtainedin the output of these analyses is contained in the Markov chains, which oneneeds to process and interpret. The marginalization technique allows us todigest these chains and compute the posterior distributions for the parametersubsets of interest. In particular, it lets us draw confidence regions intwo-dimensional planes, and get the constraints for the individual parameters.It is very well known, though, that the marginalized results can suffer fromvolume effects, which can introduce a non-negligible bias into our conclusions.The impact of these effects are barely studied in the literature. In this paperwe first illustrate the problem through a very clear and simple example in twodimensions, and suggest the use of the profile distributions (PDs) as acomplementary tool to detect marginalization biases directly from the MCchains. We apply our method to four cosmological models: the standard$\Lambda$CDM, early dark energy, coupled dark energy and the Brans-Dicke modelwith a cosmological constant. We discuss the impact of the volume effects oneach model and the cosmological tensions, using the full Planck 2018likelihood, the Pantheon compilation of supernovae of Type Ia and data onbaryon acoustic oscillations. Our test is very efficient and can be easilyapplied to any MC study. It allows us to estimate the PDs at a derisorycomputational cost not only for the main cosmological parameters, but also forthe nuisance and derived ones, and to assess the need to perform a morein-depth analysis with the exact computation of the PDs.

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