Metrics for evaluating performance and uncertainty of Bayesian network models

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Advances in Bayesian network modelling: Integration of modelling technologies (2019, background): “Error bars for BNs are termed credible intervals, which provide the range of model outcomes within a specified probability level (Marcot, 2012), but which are not often reported in BN modeling projects.”
Forecasting the relative influence of environmental and anthropogenic stressors on polar bears (2016, methodology): “We conducted sensitivity and influence analyses with the modeling shell Netica using methods and interpretations described in Marcot et al. (2006), Amstrup et al. (2008), Jay et al. (2011), and Marcot (2012).”
What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis? (2020, methodology): “We used two measures of performance because they are not necessarily correlated and can provide complementary insights into model validity (Marcot 2012).”
Inferring Missing Climate Data for Agricultural Planning Using Bayesian Networks (2018, background): “The score goes from 0 to 1, where 1 indicates the best performance [51].”