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Add negative binomial likelihood #63
Add negative binomial likelihood #63
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Codecov Report
@@ Coverage Diff @@
## master #63 +/- ##
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+ Coverage 94.66% 95.06% +0.39%
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Files 8 9 +1
Lines 75 81 +6
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+ Hits 71 77 +6
Misses 4 4
Continue to review full report at Codecov.
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Hope the dog is happy now! :) |
Apparently the keyword argument constructor breaks the test for when the second argument EDIT: Or shall I revert the constructors and we address this in a future PR? |
Change the tests similar to https://github.com/JuliaGaussianProcesses/GPLikelihoods.jl/pull/61/files#diff-d19095b0c530c06b29a86657ed746b5d40a0ebf453a75de8b02aa31b6acb119f, I'd suggest. In particular, it would be good to add tests with the keyword argument and to use also non-default values (e.g., different values for |
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Looks good, I just made some modifications in the docstring, but feel free to reject/modify them.
Can you also bump the patch version?
Co-authored-by: Théo Galy-Fajou <theo.galyfajou@gmail.com>
Co-authored-by: Théo Galy-Fajou <theo.galyfajou@gmail.com>
Co-authored-by: Théo Galy-Fajou <theo.galyfajou@gmail.com>
Co-authored-by: Théo Galy-Fajou <theo.galyfajou@gmail.com>
I opted for the p = logistic(f) parameterization. I'm not quite sure whether there are any tradeoffs in choosing this vs. e.g. \mu = exp(f), where \mu is the conditional mean of the negative binomial distribution. The latter parametrization leads to the mean to be only affected by the variance and mean of the latent GP, and not by the likelihood parameter. I haven't thought about whether this has any benefits for parameter learnability that might make it preferable to the p=logistic(f) parameterization.