How to use pymc3 for survival analysis?

[junpenglao]

It adds the log-likelihood of the observed into the model. For more information see:

pm.Potential() much needed explanation for newbie

Hi all I am relatively new in this pymc3 Bayesian space. I’ve been spending many many hours cracking what pm.Potential() actually do. I have tried this code, from my understanding so far, the posterior plot should be very very similar, but apparently not N = 1000 prob = 0.1 y_obs = np.random.binomial(n = N, p = prob) with pm.Model() as model_exp2: # model 1, the usual way # prior beta_p_M1 = pm.Beta('beta_p_M1', alpha=1, beta=1) # model 1, the regular way M1 = pm.Binomia…

and

Difference between DensityDist and Potential?

I’m currently using the Potential method to define my custom likelihood. However, I can also use Densitydist and I would like to know the difference. This thread on SO explains (if I understood correctly) CDP’s use as a way to use mock observations generated by a normal rather than having to specify actual observed data. I also briefly recall fonnesbeck mentioning that that pm.Potential does not get its own node within the graph. In Pymc2, it was used to constrain the likelihood. My questio…