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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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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.