Sample_posterior_predictive

Questions
2

It goes through each chain one at a time. However, you are not forced to supply a MultiTrace instance, you can supply a list of point dictionaries. You can get those from the trace easily doing the following:

with pm.Model():
    ...
    trace = pm.sample()
    df = pm.trace_to_dataframe(trace,
                               varnames=[the variables you want],
                               include_transformed=True)
    # We have to supply the samples kwarg because it cannot be inferred if the
    # input trace is not a MultiTrace instance
    ppc = pm.sample_posterior_predictive(trace=df.to_dict('records'),
                                         samples=len(df))

As you can see. We first get a dataframe and convert it to records dict. At the dataframe level, you can do any indexing or chain manipulation you want or need before supplying it the sample_posterior_predictive.

A related question was asked here.

1 Like