Extend support of RandomVariables in JAX backend
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ricardoV94
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ricardoV94
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Jan 24, 2023
| ) | ||
| assert test_res.pvalue > 0.1 | ||
| assert not np.isnan(test_res.statistic) | ||
| assert test_res.pvalue > 0.01 |
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The old criteria was a bit too loose. With two tests per RV it had a 81% under the null hypothesis
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Codecov Report
Additional details and impacted files@@ Coverage Diff @@
## main #200 +/- ##
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+ Coverage 79.95% 80.06% +0.11%
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Files 170 170
Lines 44856 45185 +329
Branches 9498 9608 +110
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+ Hits 35863 36176 +313
- Misses 6780 6794 +14
- Partials 2213 2215 +2
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michaelosthege
approved these changes
Jan 25, 2023
Fair enough. Those are just straightforward equivalences one can find on Wikipedia / direct translations from the Scipy and Numpy implementations. The samples are tested against the scipy CDF for the continuous distributions and I tested the first two moments (meand and std) of the discrete distributions against the moment references found on Wikipedia |
ricardoV94
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RandomVariables in JAX backend
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This PR extends the number of RandomVariables supported in the JAX backend, with two strategies:
The rewrites from 1. may be reused with other backends later (e.g, Numba). This seems like a cleaner solution than implementing the same logic in the different dispatched functions of each backend.