  We illustrate how Bayesian reweighting can be used to incorporate the
constraints provided by new measurements into a global Monte Carlo analysis of
the Standard Model Effective Field Theory (SMEFT). This method, extensively
applied to study the impact of new data on the parton distribution functions of
the proton, is here validated by means of our recent SMEFiT analysis of the top
quark sector. We show how, under well-defined conditions and for the SMEFT
operators directly sensitive to the new data, the reweighting procedure is
equivalent to a corresponding new fit. We quantify the amount of information
added to the SMEFT parameter space by means of the Shannon entropy and of the
Kolmogorov-Smirnov statistic. We investigate the dependence of our results upon
the choice of either the NNPDF or the Giele-Keller expressions of the weights.
