  In this work, we explore the use of an iterative Bayesian Monte Carlo (IBM)
procedure for nuclear data evaluation within a Talys Evaluated Nuclear data
Library (TENDL) framework. In order to identify the model and parameter
combinations that reproduce selected experimental data, different physical
models implemented within the TALYS code, were sampled and varied
simultaneously to produce random input files with unique model combinations.
All the models considered were assumed to be equal a priori. Parameters to
these models were then varied simultaneously using the TALYS code system to
produce a set of random ENDF files which were processed into x-y tables for
comparison with selected experimental data from the EXFOR database within a
Bayesian framework. To improve our fit to experimental data, we iteratively
update our 'best' file - the file that maximises the likelihood function - by
re-sampling model parameters around this file. The method proposed has been
applied for the evaluation of p+Cd-111 and Co-59 between 1 - 100 MeV incident
energy region. Finally, the adjusted files were compared with experimental data
from the EXFOR database as well as with evaluations from the TENDL-2017 and
JENDL-4.0/HE nuclear data libraries.
