Spotting the default priors in stancode returned by brms

Interfaces brms
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1

I am running a bayesian logisitc(/Bernoulli) regression model in brms, testing the effect of a series of predictors on a log-odds of meeting a specific criteria. I used the default priors supplied by brms for the model. I need to report these priors in the paper I am writing so I looked at the stancode(), which was as follows.

// generated with brms 2.14.4
functions {
}
data {
  int<lower=1> N;  // total number of observations
  int Y[N];  // response variable
  int<lower=1> K;  // number of population-level effects
  matrix[N, K] X;  // population-level design matrix
  int prior_only;  // should the likelihood be ignored?
}
transformed data {
  int Kc = K - 1;
  matrix[N, Kc] Xc;  // centered version of X without an intercept
  vector[Kc] means_X;  // column means of X before centering
  for (i in 2:K) {
    means_X[i - 1] = mean(X[, i]);
    Xc[, i - 1] = X[, i] - means_X[i - 1];
  }
}
parameters {
  vector[Kc] b;  // population-level effects
  real Intercept;  // temporary intercept for centered predictors
}
transformed parameters {
}
model {
  // likelihood including all constants
  if (!prior_only) {
    target += bernoulli_logit_glm_lpmf(Y | Xc, Intercept, b);
  }
  // priors including all constants
  target += student_t_lpdf(Intercept | 3, 0, 2.5);
}
generated quantities {
  // actual population-level intercept
  real b_Intercept = Intercept - dot_product(means_X, b);
}

Now am I right in thinking that there is a t(3,0,2.5) prior distribution on the Intercepts but a uniform prior on the b’s? The reason I ask is that I cannot see where the prior statement is for the b’s and I know from previous posts that when the prior for a parameter is not referred to in the stancode that means it has a flat uniform prior attached to it.

2

The brms function prior_summary will return a nice summary of the priors in your model.