Rename logprob/joint_logp to logprob/basic and move logcdf and icdf functions there
#6599
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ricardoV94
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logprob/joint_logp to logprob/basic and move logcdf and icdf helpers there
ricardoV94
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logprob/joint_logp to logprob/basic and move logcdf and icdf helpers therelogprob/joint_logp to logprob/basic and move logcdf and icdf functions there
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Codecov Report
Additional details and impacted files@@ Coverage Diff @@
## main #6599 +/- ##
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- Coverage 92.03% 92.02% -0.02%
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Files 93 93
Lines 15746 15750 +4
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+ Hits 14492 14494 +2
- Misses 1254 1256 +2
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Armavica
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Mar 16, 2023
| """Return the log-probability graph of a Random Variable""" | ||
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| value = pt.as_tensor_variable(value, dtype=rv.dtype) | ||
| try: | ||
| return logp_logprob(rv, value) | ||
| return logp_logprob(rv, value, **kwargs) | ||
| except NotImplementedError: |
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Let me see if I understand this correctly:
We enter this NonImplementedError in cases where the rv.owner.op has a type that was not registered anywhere with @_logprob.register(...), and so that falls back to the default (non)-implementation in abstract.py.
In these cases, why would the FunctionGraph approach work?
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It falls back to the intermediate representation (IR) which converts stuff like Exp(Beta) into MeasurableExp(Beta) (or whatever is deemed valid), which itself has a logp method dispatched.
That's basically how inference for non basic RVs is carried out.
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Ready for re-review |
Armavica
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Mar 25, 2023
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This PR is a spinoff of #6597, to facilitate review.
Important changes:
logprob/joint_logprobmodule was renamed tologprob/basic.pyIt holds all the user-facing probability constructors
logpno longer calls the heavier factorized_joint_logprob. It immediately dispatches on the IR