Trying to use pymc3 for predictive MC-Simulation [SOLVED]

Edit 2: Feel free to correct me if I am wrong, but I feel I have misunderstood “sample”. After Sample I should have a model with fit parameters, but I do not necessarily have any predictive simulations. For that, I need to do a second sample of a different type (e.x. an approx fit sample or a posterior predictive sample). So I am under the impression that the reason I didn’t have the output I expected is because I didn’t do the operation I wanted. Cheers.

Edit 3: I have tested and confirmed my suspicions. The behaviour I am looking for is pm.sample_ppc. I was using the wrong function. I’m doing a final update here in case anyone as green as I am stumbles across this via google. I am still having a couple issues I don’t understand, but they do not belong in this question. I will move them to another.


I am having trouble understanding the use of ObservedRV. I am setting up a model on data that I expect to follow a StudentT distribution, so I make the following very basic model

import pymc3 as pm
model = pm.Model()
with model:
  sd=pm.HalfNormal('sigma', 0.1)
  r=pm.StudentT('output', mu=mu, nu=nu, sd=sd, observed=train)
  trace=pm.sample(draws=60, step=step, tune=500, chains=20, cores=10)

But in the trace, I only have nu, mu, and sigma. As my model is supposed to describe r, I would expect there to be 60 values of r in the result. This is not the case. Instead, I have only the fit parameters in the trace. What am I misunderstanding? I feel like I am missing the most basic result of a monte carlo simulation.

Edit: To add a bit more information:

I would expect trace[‘output’] to give me points, but it doesn’t exist.

trace.varnames gives only
[‘sigma_log__’, ‘nu_log__’, ‘mu’, ‘sigma’, ‘nu’]
which are all expected, but still do not include any sampled values of the function I initially tried to fit to my data.

Yep, the MCMC samples in Bayesian Statistics only include free parameters. If you first encounter the concepts in Bayesian Statistics they can sometimes be quite different than what you intuitively think. Feel free to ask questions here.