# Default priors for logistic regression coefficients in brms

Hello,

I see that the default priors for logistic regression coefficients in brms is a student t with 3 degrees of freedom and a scale parameter of 10.

According to the Stan developers “Prior Choice Recommendations” Github page, the scale parameter for this prior is suggested to be between 3 and 7. I wonder why a scale parameter of 10 was chosen as the default?

I’m asking this question in part because I recently worked through chapter 11 of Statistical Rethinking (2nd Ed) in which McElreath suggests that something such as a normal(0, 1.5) prior will result in a fairly flat prior on the probability scale (see figure 11.3). Assuming I’m doing the prior predictions correctly, his student t prior set by brms still puts a lot of the density near both 0 and 1, which is making me dive into best practices for weakly informative priors in these kinds of models.

Any insight would be greatly appreciated!

I think the McElreath advice is the best. That priors page needs updated, really.

I think the state of our priors recommendations are just thinking about them in terms of the predictions you’re making (so like in logistic regression putting prior mass exclusively at 0 and 1 seems weird).

Here’s a video on it: https://www.youtube.com/watch?v=ZRpo41l02KQ&t=2694

So `normal(0, 1.5)` seems like a good starting point, but maybe that needs to change for your problem too.

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We’re working on it and also tools for making it easier to not use any default as any default can’t be universally good (no free lunch theorem holds for priors, too)

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My current 2 cents: I’ve been playing with discrete-time survival models, recently, fitting them within the logistic regression paradigm with brms. For these, the probabilities are usually on the low side and `normal(0, 1.5)` pushes the posterior around more aggressively than I’d like. It appears something like `student_t(6, 0, 1.5)` works okay.

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