Hi! For a simple bayesian logistic regression model:
def logistic_regression(data, labels=None, D): coefs = numpyro.sample('coefs', dist.Normal(jnp.zeros(D), jnp.ones(D))) intercept = numpyro.sample('intercept', dist.Normal(0., 10.)) logits = jnp.sum(coefs * data + intercept, axis=-1) return numpyro.sample('obs', dist.Bernoulli(logits=logits), obs=labels)
how does one get
'coefs' as well as
'obs' during the use of
predictive()? For example, right now I’m trying to get 1000 predictions (samples) given a single test input via HMC. I get a list of predictions but I would also like to get, for each prediction, the coefs for that prediction. So something like a (1000, D) shaped array. Thanks.