Infer logp and logcdf of abs of discrete variables #7958
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logcdf(x=lower) != -inf
ricardoV94
commented
Nov 10, 2025
| ndim_supp = value.ndim - input_logprob.ndim | ||
| jacobian = jacobian.sum(axis=tuple(range(-ndim_supp, 0))) | ||
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| # Discrete transformations do not need the jacobian adjustment |
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We don't really support any discrete cases with det(jacobian) != 1 but I prefer to have this branch already
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jessegrabowski
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Nov 11, 2025
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I found a typo! I'm helping!
| else: | ||
| x_rv = pt.abs(DiscreteUniform.dist(-4, 4, size=(5,))) | ||
| # |x_rv| = DiscreteUniform(0,4) with P(X=0) halved relative to other values | ||
| # We can use a Categorical to representh this |
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| # We can use a Categorical to representh this | |
| # We can use a Categorical to represent this |
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I was going to work on general icdf(abs(x)) which requires an optimization routine, but found some small crumbs along the way.