Translating Censored Data example from PyMC2 to PyMC3

Hi there,

I have found a great example of Bayesian parametric survival analysis in pymc2 here:

I want to reproduce it in pymc3. I am struggling with encoding the censored events. How would you translate this code to PyMC3?

N = 2500 #125 times more data than before
lifetime = mc.rweibull( 2, 5, size = N )
birth = mc.runiform(0, 10, N)
censor = ((birth + lifetime) >= 10) #an individual is right-censored if this is True 
lifetime_ = lifetime.copy()
lifetime_[censor] = 10 - birth[censor] #we only see this part of their lives.

alpha = mc.Uniform('alpha', 0, 20) #lets just use uninformative priors again
beta = mc.Uniform('beta', 0, 20)

def survival(value=lifetime_, alpha = alpha, beta = beta ):
    return sum( (1-censor)(log( alpha/beta) + (alpha-1)*log(value/beta)) - (value/beta)**(alpha)

mcmc = mc.MCMC([alpha, beta, survival ] )
mcmc.sample(50000, 30000)

My understanding is that the @mc.observed decorator is not supported in PyMC3.

How would you translate this code?
What is the most elegant and scalable way to handle censored data in PyMC3?


Hi Tokoro,
There is a quite detail example of modelling censored data in the PyMC3/examples, it should answer most of your question.


Thanks for the quick reply and for the pointer. I do think the answer is in that example. I will tinker a bit with it and come back here if I have any further questions.

Thank you!