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
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 #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!