Ah, that would be the common sense solution but I’ve got a bit of an edge case. My observed data is a relatively large array and removing the missing entries would make it into a ragged array. This appears to make the sampling that I do much less efficient when I instantiate a new random variable for each row of the ragged array. In Tensorflow Probability I’ve accommodated this by simply applying an elementwise mask to zero out terms in the target log posterior density before sampling.
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