Bayesian probability updates using sampling/importance resampling: Applications in nuclear theory

We review an established Bayesian sampling method called sampling/importance resampling and highlight situations in nuclear theory when it can be particularly useful. To this end we both analyse a toy problem and demonstrate realistic applications of importance resampling to infer the posterior distribution for parameters of delta NNLO interaction model based on chiral effective field theory and to estimate the posterior probability distribution of target observables. The limitation of the method is also showcased in extreme situations where importance resampling breaks.

Automated summary

We introduce a Bayesian sampling method called sampling/importance resampling and discuss its specific usefulness in nuclear theory. By analyzing a toy problem and presenting realistic applications, we demonstrate the method's effectiveness in inferring posterior distributions and estimating posterior probability distributions. However, we also highlight the limitations of the method in extreme situations where it breaks.