Comments (2)
Actually, our fori_append
output + transform
functionalities are quite convenient for this job. The following code plots well the result.
hmc_states = fori_append(sample_kernel, hmc_state, num_samples,
transform=lambda x: {"z": x.z})
import arviz as az
data = arviz.from_dict(hmc_states)
az.plot_trace(data);
@neerajprad I think we should make a notebook for bayesian regression using arviz instead of making custom functions as in pyro's bayesian regression :)
from numpyro.
Good idea. I think since we are using very straightforward data structures, it is best to delegate all the diagnostics / plotting to arviz.
from numpyro.
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from numpyro.