bayesian - Hyperpriors for hierarchical models with Stan -


i'm looking fit model estimate multiple probabilities binomial data stan. using beta priors each probability, i've been reading using hyperpriors pool information , encourage shrinkage on estimates.

i've seen example define hyperprior in pymc, i'm not sure how similar stan

@pymc.stochastic(dtype=np.float64) def beta_priors(value=[1.0, 1.0]):     a, b = value     if <= 0 or b <= 0:         return -np.inf     else:         return np.log(np.power((a + b), -2.5))  = beta_priors[0] b = beta_priors[1]

with , b being used parameters beta prior.

can give me pointers on how similar done stan?

following suggestions in comments i'm not sure follow approach, reference thought i'd @ least post answer question of how accomplished in stan.

after asking around on stan discourses , further investigation found solution set custom density distribution , use target += syntax. equivalent stan of example pymc be:

parameters {   real<lower=0> a;   real<lower=0> b;   real<lower=0,upper=1> p;   ... }  model {   target += log((a + b)^-2.5);    p ~ beta(a,b)   ... }

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