Disabling missing data imputation

Questions
6

For future reference, here is an example of using a binary mask to ignore some values in the calculation of the posterior density:

import numpy as np
import pymc3 as pm

n = 30
p = 3

fraction_kept = 0.75

beta_true = np.random.randn(p)
X = np.random.randn(n,p)
y = np.dot(X,beta_true) + np.random.randn(n)

mask = np.random.binomial(n,fraction_missing,y.shape)

with pm.Model() as model:
    beta   = pm.Normal('beta',shape=p)
    err_sd = pm.HalfCauchy('err_sd',beta=1)
    y_hat  = pm.math.dot(X,beta)
    
    likelihood = pm.Potential('likelihood',pm.Normal.dist(mu=y_hat, sd=err_sd).logp(y)*mask)
    trace = pm.sample()
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