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Issue 350: Optimise stan via profiling #501
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…ercept parameterisation
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Co-authored-by: Jessalyn Sebastian <116767845+jessalynnsebastian@users.noreply.github.com>
This is how benchmark results would change (along with a 95% confidence interval in relative change) if c5290e4 is merged into main:
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This is how benchmark results would change (along with a 95% confidence interval in relative change) if 1fd88d0 is merged into main:
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All looks good to me after those minor changes
Going to monitor this to see if we see any performance impacts from the change in phi prior |
Description
This PR is related to #350 but doesn't close it as there is more work to do.
Using profiling I have identified areas that are bottlenecking the code (mostly
expected_obs
) and performance tuned with the slight cost of readability.I have also looked at the trajectory and posteriors and identified that our diffuse prior on the overdispersion may lead to some significant slow down. Given we have low levels of belief in most settings I propose we moderately tigthen it. In terms of posterior I have seen no impact of this.
I have also gone through all the case studies and examples and checked the
adapt_delta
andmax_treedepth
settings. In most cases I have been able to relax these with no diagnostic issues.Tagged a few reviewers who may be interested in taking a look. @zsusswein may also be interested. @jessalynnsebastian I pinged you as I edited some of the tests and docs for
expected_obs()
in this PR which you recently touched.Checklist