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998 abc methods for trial based data using statistical distances #1104
998 abc methods for trial based data using statistical distances #1104
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… based on regularized optimal transport
…hods-for-trial-based-data-using-statistical-distances
…hods-for-trial-based-data-using-statistical-distances
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Thanks a lot, this is great!
Added a couple of comments.
…hods-for-trial-based-data-using-statistical-distances
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Great, thanks for the edits!
I added some last comments, once those addressed, the PR can be merged 🎉
Ping @theogruner 🙂 We are so close to merging here. |
Ping @theogruner will you have time to finish this soon? |
…hods-for-trial-based-data-using-statistical-distances
Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #1104 +/- ##
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- Coverage 83.08% 72.94% -10.14%
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Files 92 93 +1
Lines 7259 7371 +112
===========================================
- Hits 6031 5377 -654
- Misses 1228 1994 +766
Flags with carried forward coverage won't be shown. Click here to find out more.
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Hi, |
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Great, looks good now! Thanks a lot for adding these features 👏
What does this implement/fix? Explain your changes
Adding an approximation of the squared 2-Wasserstein distance based on Sinkhorn iterations as an additional statistical distance to the available metrics. Furthermore, extending MCABC and SMCABC to allow conditioning on multiple observations using statistical distances.
Does this close any currently open issues?
Fixes #998
Checklist
guidelines
with
pytest.mark.slow
.guidelines
main
(or there are no conflicts withmain
)