Add one-sided/two-sided truncated distribution and cdf/icdf method for univariate symmetric distributions#915
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@fritzo Could you help me review this PR? Pls consider this as a low priority PR. A nice thing is your |
fritzo
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Feb 16, 2021
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Interface looks great! I haven't checked math of .cdf(),.icdf() but I would feel more confident if we added some simple algebraic tests independent of SciPy, testing with a large grid of random values.
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| # if low < loc, returns cdf(high) = 1; otherwise returns 1 - cdf(high) = 0 | ||
| loc = self.base_dist.loc | ||
| sign = jnp.where(loc >= self.low, 1., -1.) | ||
| return 0.5 * (1 + sign) |
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why not simply
return jnp.where(self.low < self.base_dist.loc, 1., 0.)
fehiepsi
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Feb 17, 2021
| quantiles = random.uniform(random.PRNGKey(1), (100,) + d.shape()) | ||
| try: | ||
| if d.shape() == (): | ||
| rtol = 1e-3 if jax_dist is dist.StudentT else 1e-5 |
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rtol=1e-5 fails for one StudentT test. I guess the precision of betainc/its grad is not good enough or samples got extreme values.
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Feb 17, 2021
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Resolves #895, #894, #914. The implementation only supports symmetric distributions because we can leverage the symmetry to address numerical issues that are discussed at probtorch for the lower bound > 5. The issue is cdf/icdf suffers from precision errors at the right tail (e.g.
Normal().cdf(6.) = 1.). Thanks to the symmetry, we can transform the right tail to the left tail, which has better precision (e.g.Normal().cdf(-6.) = 9.865896e-10).TODO