3) We introduce a simple testing procedure based on the posterior probability of the hypothesis that can be seamlessly combined with MCMC. Aside from having some neat properties, it can also be easily transformed into a Bayes factor.

1) We propose a method for building nonparametric hypotheses that consider researcher's opinions. For example, if the data differs from a normal by at most 1% and that is good enough for the researcher to assume normality, this can be easily inserted into the hypothesis!

I am happy to announce that a paper of mine, Rafael B. Stern and @rafael-izbicki.bsky.social has been accepted for publication at the International Journal of Approximate Reasoning 🎉

In our paper, we show how to build and test hypotheses of practical equivalence in nonparametric settings. 🧵

post a meme made by you

This one was inspired by arxiv.org/abs/2407.02754

Yep!