Bayesian correction of \(H(z)\) data uncertainties

We compile 41 \(H(z)\) data from literature and use them to constrain O\(\Lambda\)CDM and flat \(\Lambda\)CDM parameters. We show that the available \(H(z)\) suffers from uncertainties overestimation and propose a Bayesian method to reduce them. As a result of this method, using \(H(z)\) only, we find, in the context of O\(\Lambda\)CDM, \(H_0=69.5\pm2.5\mathrm{\,km\,s^{-1}Mpc^{-1}}\), \(\Omega_m=0.242\pm0.036\) and \(\Omega_\Lambda=0.68\pm0.14\). In the context of flat \(\Lambda\)CDM model, we have found \(H_0=70.4\pm1.2\mathrm{\,km\,s^{-1}Mpc^{-1}}\) and \(\Omega_m=0.256\pm0.014\). This corresponds to an uncertainty reduction of up to 30\% when compared to the uncorrected analysis in both cases.