Use un-pivoted Cholesky triangle to sample from sparse MvNormalCanon #1218
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@@ Coverage Diff @@
## master #1218 +/- ##
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- Coverage 81.79% 79.97% -1.82%
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Files 116 116
Lines 6558 5963 -595
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- Hits 5364 4769 -595
Misses 1194 1194
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Bump...@andreasnoack, does this need anything else before it can merge? |
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No. Looks good. |
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Fixes #1200.
Note I also rewrote the tests--the SuiteSparse Cholesky decomposition returns a different decomposition than the
Base.LinearAlgebrafunction (since the Cholesky factorization is not in general unique). As a result, the random samples from anMvNormalCanondistribution with a sparse precision matrix are not in general identical to those from anMvNormalCanonorMvNormal, even if the seeds are identical. As a result, these tests only check for approximate statistical equality, rather than strict numerical equality of the samples. Not sure if there's a better way to handle that.