Add LogNormal#84
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LogNormal distribution parameterized by the loc/scale of the underlying Normal, with sample, log_prob, cdf/icdf, entropy, moments, and KL divergence (computed via the invariance of KL under the strictly monotonic log transform).
lockwo
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Jul 6, 2026
| `loc` and `scale` of the underlying Normal. | ||
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| loc: Array |
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can type as Float[Array]? also can do the converter trick mentioned in another PR to remove init
| dtype = jnp.result_type(self.loc, self.scale) | ||
| return jax.random.normal(key, shape=self.event_shape, dtype=dtype) | ||
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| def sample(self, key: Key[Array, ""]) -> Array: |
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event_shape is loc.shape, but sample() later broadcasts with scale. e.g. LogNormal(0, jnp.ones(3)).event_shape == (), while sample() returns shape (3,) with the same random draw repeated across all entries. Either use the broadcasted parameter shape for sampling, or reject unequal parameter shapes
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