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When we use pytensor.config.floatX=="float32", integer data is downcast to "int16" which has a pretty limited range of 32k. For count-based likelihoods this is way too narrow. I am not sure we should be doing anything with integers to begin with. Why are PyTensor casting rules (and customization flags) not sufficient?
The text was updated successfully, but these errors were encountered:
Description
When we use
pytensor.config.floatX=="float32"
, integer data is downcast to"int16"
which has a pretty limited range of 32k. For count-based likelihoods this is way too narrow. I am not sure we should be doing anything with integers to begin with. Why are PyTensor casting rules (and customization flags) not sufficient?The text was updated successfully, but these errors were encountered: