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In this work, a method is proposed for combining differential and integralbenchmark experimental data within a Bayesian framework for nuclear dataadjustments and multi-level uncertainty propagation using the Total Monte Carlomethod. First, input parameters to basic nuclear physics models implementedwithin the state of the art nuclear reactions code, TALYS, were sampled fromuniform distributions and randomly varied to produce a large set of randomnuclear data files. Next, a probabilistic data assimilation was carried out bycomputing the likelihood function for each random nuclear data file based firston only differential experimental data (1st update) and then on integralbenchmark data (2nd update). The individual likelihood functions from the twoupdates were then combined into a global likelihood function which was used forthe selection of the final 'best' file. The proposed method has been appliedfor the adjustment of $^{208}$Pb in the fast neutron energy region below 20MeV. The 'best' file from the adjustments was compared with availableexperimental data from the EXFOR database as well as evaluations from the majornuclear data libraries and found to compare favourably.

Bayesian updating for data adjustments and multi-level uncertainty propagation within Total Monte Carlo