Daniel 🕹️6d ago
A new paper by @dominiquemakowski.bsky.social, @mattansb.msbstats.info, me and colleagues just out! We show how to choose informative priors in Bayesian regression models using a systematic simulation study and a practical step-by-step tutorial in #Rstats and #Stan! doi.org/10.3389/fpsy... >
Show threadDaniel 🕹️6d ago
Small samples or rare events making your frequentist models unstable? In our paper, we show how Bayesian informative priors act as a regularizing force, narrowing credible intervals and preventing implausible estimates when data is sparse. >
Show threadDaniel 🕹️6d ago
Subjectivity concerns holding you back from Bayesian methods? Our tutorial walks through a real-world case study using "believer", "agnostic", and "skeptical" priors to run a built-in sensitivity analysis - ensuring robust and transparent science >
Show threadDaniel 🕹️
Bayesian priors aren't just arbitrary guesses - you can (and should) validate them. Our paper shows how to use prior predictive checks to map your domain knowledge onto the model, ensuring your assumptions generate realistic, well-calibrated priors. #Rstats #Stan #Bayes
Jun 24 at 9:12amWeb
Show threadDaniel 🕹️6d ago
All material (code, data) is freely available at osf.io/8evy5/, but you can also use the easy-to-use function `check_priors()` in the #easystats {performance}📦 to conduct prior predictive checks: easystats.github.io/performance/...

Prior predictive checks — chec...
Prior predictive checks — check_priors

Simulates from the prior marginal distribution of the data to assess the consistency of the chosen priors with domain knowledge (Gabry et al. 2019, Lüdecke et al. 2026) and creates a visualization from the prior predictive checks.