Fix Beta with concentration1=1 gives nan log_prob at value=0 #2089
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juanitorduz
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bug
fehiepsi
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Nov 1, 2025
numpyro/distributions/continuous.py
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| return self._dirichlet.log_prob(jnp.stack([value, 1.0 - value], -1)) | ||
| # Handle edge cases where concentration1=1 and value=0, or concentration0=1 and value=1 | ||
| # These cases would result in nan due to log(0) * 0 in the Dirichlet computation | ||
| log_prob = jnp.where( |
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interesting! could you check if grads w.r.t. value, concentration1, concentration0 are not NaN?
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Good catch 🙈 ! The gradients are NaN with this approach. After some investigation and talking with Claude Code 😅 . We arrived to a solution via https://docs.jax.dev/en/latest/_autosummary/jax.custom_jvp.html, see 13fc288 and 19f8be7
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Closes #2088