Hi PyMC3ers,

I’ve got a large number of models (one model fit on multiple datasets), and would rather not run `pm.sample_posterior_predictive`

on each one in order to get samples.

Is it “OK” to just use the actual samples returned by `pm.sample`

instead?

What I really want is the range of estimated parameters for a StudentT distribution, which I can then throw into a scipy.stats.t object to get to the `ppf`

method. For example:

```
from scipy.stats import t
trace = traces[15] #get the trace for the 15th model
mu = trace['mu'] #these are the values returned by pm.sample
nu = trace['nu'] #these are the values returned by pm.sample
scale = trace['sig'] #these are the values returned by pm.sample
studentT = t(nu,mu,sig) #get an array of t distributions parameterized by those samples above
#now find the ppf value for 0.4 for each of these t distributions:
ppf_values = studenT.ppf(0.4)
```

Thanks for your time!