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Add tutorial for models with missing data #415
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I think the issue in Pyro refers to allowing a way to support models with sample statements of the kind |
I will use the most naive way to impute those missing data. For example,
I am not sure how to make this possible with |
I see - I think your method will give us the ML estimate for the missing values.
I am not quite sure, but maybe sampling from the
In any case, I think this will make for a good tutorial. |
Actually, those imputed values will have priors
acts on the "merged" x (x_notnan & x_impute). Here I am using the fact that in NumPyro (not Pyro) MCMC
and
are equivalent (even for priors with constrained supports). This way also works for multivariate priors IIUC. The script in your comment will double log_prob of |
Interesting. No, you are absolutely right about the double counting. What I wrote above is incorrect unless we are doing masked observes. Actually, your original solution is quite clever! This will make for an interesting tutorial, look forward to it. |
We should able to run MCMC for models with missing data in NumPyro because NumPyro supports improper priors through
param
primitive. This is also a good chance to illustrate that NumPyro supports improper priors.Related issues in other repos:
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