I screwed around with trying to compute Bayes factors for models of distributions over set partitions, having been led astray by Bayesian phylogenetic inference

I screwed around with trying to compute Bayes factors for models of distributions over set partitions, having been led astray by Bayesian phylogenetic inference methods. It was a waste of time--in practice the epistemology was terrible because the choice of prior distributions had such a huge effect on model comparisons. On top of that, the computations were highly unstable so I had to do a lot of fancy multi-temperature MCMC stuff that never quite worked. Unless your priors are based on actual observations, stick with model selection approaches that are based on measured predictive power, or at least plausible approximations thereof, e.g. Aki Vehtari et al. LOO-CV (approximate leave-one-out cross-validation): https://avehtari.github.io/modelselection/ https://avehtari.github.io/modelselection/ https://mc-stan.org/loo/ https://mc-stan.org/loo/