I am working through the pymc3 Bayesian Survival Analysis example at the link below and I’m struggling to understand their use of
The heart of the example comes down to the following two snippets of code:
y_obs = pm.Gumbel("y_obs", η[~cens_], s, observed=y_std[~cens]) y_cens = pm.Potential("y_cens", gumbel_sf(y_std[cens], η[cens_], s))
Basically what is being done is they are modeling observed survival as a Gumbel distribution and censoring as a Gumbel survival function. In general that sort of makes sense to me. I do think I understand how observed deaths and observed censoring mathematically relate to these distributions. I just don’t understand what
pm.Potential is doing. In fact, if I take it out it doesn’t appear to affect anything.
Can anyone educate me on the function of
pm.Potential in the above example? I watched @junpenglao’s PyData but the above still wasn’t clear.