Stop Guessing at Priors: R2D2’s Automated Approach to Bayesian Modeling
Alternatively titled: How I Stopped Worrying and Learned to Love Variance Allocation
Hello fellow datanistas!
Have you ever stared at a Bayesian model and wondered, "How on earth am I supposed to set all these priors?" I’ve been there—tweaking hyperparameters, second-guessing every choice, and wishing for a more intuitive way. That’s when I learned about the R2D2 framework, and it completely changed how I think about Bayesian regularization.
In this post, I share my journey exploring the R2D2 family of priors—from the original shrinkage prior to its extensions for generalized linear and multilevel models. My goal is to demystify how R2D2 lets you specify your beliefs about model fit directly, and how its elegant mathematical structure adapts to increasingly complex modeling scenarios. If you’ve ever felt lost in a sea of coefficient priors, this is for you.
The R2D2 framework (Zhang et al., 2020) grabbed my attention because it flips the usual Bayesian script: instead of guessing priors for every coefficient, you place a prior on the model’s R²—how much variance you expect your model to explain. This single, intuitive choice then drives the rest of the model, with a Dirichlet decomposition automatically allocating explained variance across predictors.
What’s beautiful is how this idea extends. For generalized linear models, the framework adapts by approximating the noise structure, preserving the same R² intuition even when outcomes aren’t Gaussian. And for multilevel models (think: experiments with multiple grouping factors), the R2D2M2 extension allocates variance across types of effects, not just individual predictors—solving the overfitting problem that plagues traditional approaches.
Throughout my exploration, I was struck by the mathematical elegance: each extension tweaks just what’s needed, keeping the core idea intact. The result? A unified, interpretable, and practical approach to Bayesian modeling that scales from simple regressions to the most complex experimental designs.
If you’re a laboratory researcher or anyone wrangling with hierarchical data, R2D2M2 is especially powerful. It tells you, quantitatively, which factors matter most—so you can focus your scientific efforts where they count.
R2D2 and its extensions let you control model fit with a single, intuitive prior on R²—automatically distributing explanatory power across predictors and effect types, and making Bayesian modeling more accessible and interpretable.
Have you struggled with setting priors in complex models? What strategies or frameworks have helped you, and where do you still get stuck? I’d love to hear your experiences.
Curious about the full mathematical details and practical code examples? Read the complete post here: Stop Guessing at Priors: R2D2’s Automated Approach to Bayesian Modeling. If you found this helpful, please share with a fellow datanista or subscribe for more deep dives.
Cheers,
Eric