MultiHub Forum

I'm a postdoctoral researcher in cognitive neuroscience, and I'm trying to shift our lab's analysis pipeline from traditional frequentist methods to using Bayesian statistics for our fMRI and behavioral data. I understand the theoretical advantages, but I'm struggling with the practical implementation, especially choosing appropriate priors and communicating the results to a field still dominated by p-values. For researchers who have made this transition, what software or libraries did you find most robust for complex hierarchical models? How do you approach justifying and setting priors in a way that satisfies reviewers skeptical of Bayesian methods, and what are the most effective ways to present results like posterior distributions and Bayes factors in papers and presentations? Are there any common pitfalls in model checking or computational efficiency you wish you'd known about earlier?