Alex Naka (@gottapatchemall.bsky.social)

New blog post (and uh, new blog!) If you use observation-level random effects (e.g., for over-dispersed counts), your predictive checks are probably wrong. Here's how to do it the right way in both #brms and #PyMC. tl;dr: you need to sample new levels of the OLRE to avoid a false goodness-of-fit

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The right way to do predictive checks with observation-level random effects – Erik J. Ringen, PhD https://erikringen.github.io/posts/OLRE/