Stabilize Negative Binomial with large mu/alpha - #8394
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #8394 +/- ##
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+ Coverage 91.83% 91.85% +0.01%
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Files 128 128
Lines 21259 21259
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+ Hits 19523 19527 +4
+ Misses 1736 1732 -4
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ricardoV94
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Aug 9, 2026
| # (1 - p) / p in log space: a p = sigmoid(w) that saturates at 1.0 carries no | ||
| # information, while the rewritten log terms still see w. Spelled log(1 - p) | ||
| # because the sigmoid stabilization rewrites do not recognize log1p(-p) | ||
| mu = n * pt.exp(pt.log(1 - p) - pt.log(p)) |
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| @classmethod | ||
| def get_n_p(cls, mu=None, alpha=None, p=None, n=None): | ||
| def get_n_p(cls, mu=None, alpha=None, p=None, n=None, math=pt): |
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what's the story with the math argument?
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tensor and xtensor are using the helper
| def test_zeroinflatednegativebinomial_logp(self): | ||
| def logp_fn(value, psi, mu, alpha): | ||
| n, p = NegativeBinomial.get_n_p(mu=mu, alpha=alpha) | ||
| n, p = alpha, alpha / (mu + alpha) |
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Why don't we want to use the helper anymore?
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It was just convenience for the test, there's no numpy.sigmoid so can't be used anymore for bon symbolic inputs
jessegrabowski
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Aug 10, 2026
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