where, the probabilities associated with the measured data cancel out, and the relative posterior probabilities depends only on the evidence and prior probabilities for each hypothesis. Throughout this work, we have taken the ratio of the prior probabilities to be unity; suggesting that all models are equally likely. Therefore, it is only necessary to determine the evidence for a model concerning the given data. Dynamic nested sampling was developed by Higson et al. , building on the work of Skilling , and is implemented in the dynesty Python package . This method enables the efficient estimation of the integral over the likelihood, L(X|H) and parameter specific prior p(X|H), which is gives the evidence ,