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Chevychase #48
Chevychase #48
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Codecov Report
@@ Coverage Diff @@
## master #48 +/- ##
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Coverage 99.17% 99.17%
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Files 34 34
Lines 1330 1341 +11
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+ Hits 1319 1330 +11
Misses 11 11
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Looks good to me. I'm not crazy about including the standalone plotting script diagnostics.py
, but if you find it useful I think its fine. It's at least in the testing > data subdirectory specific to french-wilson so its context is fairly clear.
This pull request will
pymc3
The major point is that we now have a much larger gold standard test data set in the form of Markov chain Monte Carlo integration performed in
pymc3
. I've ensured that the output of our algorithm stays very close to the MCMC output. I've improved numerical stability and decreased memory requirement by using Gauss-Chebyshev quadrature with degree 100. It is possible we can lower this degree setting if there are performance concerns. Currently, all posterior parameters are within 2.5% of the MCMC values as compared to 6% for the cctbx implementation of the classical algorithm.I think this implementation constitutes a new state of the art. Two things we could potentially do to improve down the line:
Both of these things are set quite arbitrarily right now.
Here are some histograms of percent errors between
rs
and MCMC for the four posterior parameter estimates: