How to extract Posterior samples of Log Likelihood from PyStan?

Question

I need posterior samples of log likelihood terms to run PSIS here such that

log_lik : ndarray
    Array of size n x m containing n posterior samples of the log likelihood
    terms :math:`p(y_i|\theta^s)`.

where small example here is such that pip install pystan and

import pystan
schools_code = """
data {
    int<lower=0> J; // number of schools
    real y[J]; // estimated treatment effects
    real<lower=0> sigma[J]; // s.e. of effect estimates
}
parameters {
    real mu;
    real<lower=0> tau;
    real eta[J];
}
transformed parameters {
    real theta[J];
    for (j in 1:J)
    theta[j] = mu + tau * eta[j];
}
model {
    eta ~ normal(0, 1);
    y ~ normal(theta, sigma);
}
"""

schools_dat = {'J': 8,
               'y': [28,  8, -3,  7, -1,  1, 18, 12],
               'sigma': [15, 10, 16, 11,  9, 11, 10, 18]}

sm = pystan.StanModel(model_code=schools_code)
fit = sm.sampling(data=schools_dat, iter=1000, chains=4)

How can I get the posterior samples of Log Likelihood of the PyStan fit model?

Answer 1

You can get the posterior samples of Log-Likelihood by doing: logp = fit.extract()['lp__']