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Adding large GPMSA verification example #615

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merged 5 commits into from
Aug 2, 2017

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Replaces #607.

@dmcdougall dmcdougall added this to the v0.58.0 milestone Jul 27, 2017

// Read in data and store the standard deviation of the simulation
// data (ignored for now).
readData("sim_scalar.dat", "y_exp_scalar.txt", simulationScenarios, paramVecs,
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Are these files included in the PR anywhere?

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Wait, never mind, those are the files that were merged in #580.

@dmcdougall dmcdougall force-pushed the gpmsa_pdf_verif_large branch from 0b19994 to f50e7fa Compare August 2, 2017 03:19
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codecov bot commented Aug 2, 2017

Codecov Report

Merging #615 into dev will decrease coverage by 0.2%.
The diff coverage is 92.3%.

Impacted file tree graph

@@            Coverage Diff             @@
##              dev     #615      +/-   ##
==========================================
- Coverage   75.07%   74.87%   -0.21%     
==========================================
  Files         311      312       +1     
  Lines       23528    23757     +229     
==========================================
+ Hits        17664    17788     +124     
- Misses       5864     5969     +105
Impacted Files Coverage Δ
test/test_gpmsa/scalar_pdf_small.C 54.27% <100%> (-1.15%) ⬇️
test/test_gpmsa/scalar_pdf_large.C 55.31% <92.15%> (ø)

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@dmcdougall dmcdougall force-pushed the gpmsa_pdf_verif_large branch from 326b425 to a5d4fb7 Compare August 2, 2017 03:40
For GPMSA, we expect our log prior and matlab's log prior to differ only
by an additive constant in log space.  I've added a comment that
explains this.
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@roystgnr Ok I think I'm done here too. Let me know if you have any comments.

@roystgnr roystgnr merged commit da8e014 into libqueso:dev Aug 2, 2017
@dmcdougall dmcdougall deleted the gpmsa_pdf_verif_large branch August 2, 2017 22:33
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@briadam This is the pull request where I destroyed all your hard work. I'll resurrect it.

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briadam commented Sep 13, 2017

Hardly destroyed... just morphed it into a necessary prerequisite test. ;-)

Honestly, you'll probably do better to just write an inverse problem departing from scalar_pdf_large without looking at what I did as you'll likely then immediately see the issues I ran into with the initial positions and proposal covariance. I can see having to scale the proposal covariance as we discussed today, but it's possible at this point (given all the work you've done) that if you take the initial point at the mean and proposal covariance to be the prior variance, you may fare better than I did, and it'll just work.

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Well, it'll be a pull request in a couple of minutes and then you can tell me what to do with it :)

@dmcdougall dmcdougall restored the gpmsa_pdf_verif_large branch October 4, 2017 23:09
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3 participants