Add Empirical#63
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lockwo
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Apr 5, 2026
| self.rtol == 0, self.atol, self.atol + self.rtol * jnp.abs(value) | ||
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| def sample_and_log_prob(self, key: Key[Array, ""]) -> tuple[Array, Array]: |
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for these functions (that do the same as the default approach), we can use the mixin approach to inheritance (e.g. inherit from AbstractSampleLogProbDistribution as well, this can be done for multiple classes)
| import jax | ||
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| # Must be set before any JAX arrays are initialized | ||
| jax.config.update("jax_enable_x64", True) |
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if memory serves, this can be spotty when enabled in pytest files, if you need it, we can put in a conftest.py
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I think making sure the numerics check out for non 0 values of empirical rtol/atol should have additional tests
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- AbstractEmpirical now inherits AbstractSampleLogProbDistribution's default sample_and_log_prob instead of duplicating it. - Move jax_enable_x64 config into tests/conftest.py (more reliable than per-file config, per lockwo's suggestion) and drop the now-redundant per-file config lines fork-wide. - Add rtol/atol tolerance tests for Empirical.
Encapsulate a set of observed samples as a distribution, with support for weighted samples. Referenced against TensorFlow Probability's Empirical. AbstractEmpirical inherits AbstractSampleLogProbDistribution for its default sample_and_log_prob implementation.
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This PR adds AbstractEmpirical, Empirical and WeightedEmpirical distributions.
These distributions encapsulate a set of observed samples of a variable. The math was mirrored off tfp.distributions.Empirical with additional support for weighted samples.