Set an informative prior

This is my first attempt with Bayesian statistics. I need to figure out if I have enough information to set more informative priors for a zero-one-inflated beta regression in brms. Initially I used the default priors (coi: beta(1, 1), Intercept: student_t(3, 0, 10), phi: gamma(0.01, 0.01), sd: student_t(3, 0, 10), zoi: beta(1, 1)), which according to my understanding are uninformative and have no impact on the posterior. I saw that many people in Bayesian statistics encourage researchers to think about using more informative priors, so I wonder if I could derive something from a previous study I did, with the same dependent variables.

Previous study:
The stimulus was a set of 36 clips that had been selected subjectively by experimenters to fit into 9 classes which were combinations of two 3-level categoricals: pleasure (negative, neutral, positive) and intensity (low, medium, high). There were 6 clips in each class. The participants watched the clips and evaluated them in terms of pleasure and intensity on two continuous sliders with values in [0,1].

Current study:
The stimulus is a set of 18 clips that have been generated from the clips of the previous study, and the generative model was conditioned on the pleasure/intensity continuous scores given by the participants of the previous study. The clips are labeled with 3-level categorical tags, p_cat (negative, neutral, positive) and i_cat (low, medium, high), that were derived from the discretization of conditioning continuous scores in 3 bins. Current participants evaluated them as in the previous study: pleasure and intensity in two continuous sliders with values in [0,1]. Here is my current model:

mvbind(pleasure, intensity) ~  p_cat * i_cat + age + experience + gender + (1|item) + (1 |subject)

where p_cat and i_cat are the 3-level categories for pleasure and intensity.

  1. Can I use information from the distribution of the response in the previous study as a prior to the current study?

  2. In case that would be an acceptable approach, would it make sense to add priors on the coefficients for each level of my categorical variables p_cat and i_cat? For instance, in terms of p_cat and the response pleasure, a prior for the coefficient of Intercept (reference level for negative p_cat), another for the coefficient of neutral p_cat, and a third one for positive p_cat.

  3. If this is acceptable, can I get all the responses from previous study, split them in 3 bins, get the mean and variance of each bin and use this to set the priors mentioned in 2?

  • Operating System: Ubuntu 16.04.6 LTS
  • brms Version: 2.10.0

I think the current basic recommendation for priors is to check what your prior implies in terms of predictions from your model (prior predictive checks):

So in terms of getting priors, just make a rough guess using normals and then see what the predictions are. If you get crazy stuff wiggle things up or down and see what happens.

It might seem unsatisfactory to do something like this cause it’s like doing a really rough inference in your head haha, But don’t try to fit to data or anything. Just make sure your predictions are on the right scale and such.

Well presumably you could, but the technical aspect of this question isn’t the tricky part. The onus is on you to answer if that makes sense in your application and report what you did appropriately. I guess that’s the tricky part haha.

You probably want priors everywhere, yes. I don’t know if specifying them separately is possible in brms or rstanarm (I doubt it).

That’s up to you!

It really sounds like what you want to do is fit two models to two sets of data at once. You can do that in Stan. Get both models working on their own and then lump em’ together and share the parameters. Taking a posterior from one inference and using it in another isn’t really something we do so much (cause we don’t really have the posterior distribution itself as an object).

also relevant: How to pass parameter estimates from one model to another at every MCMC iteration?