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add option for using posterior predictive in cross-validation #2517

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sdaulton
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Summary:
see title. This change is particularly important for model selection using the NLL if we have noisy observations. Using the posterior over the true function and not the noisy observations gives quite misleading results about model calibration.

I also think that predicted vs actual plots from LOOCV are insightful when using the posterior predictive when the observations are noisy. We may want to consider adding observation_noise to predict, but we can do that in a follow-up.

Differential Revision: D58227612

@facebook-github-bot facebook-github-bot added the CLA Signed Do not delete this pull request or issue due to inactivity. label Jun 13, 2024
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This pull request was exported from Phabricator. Differential Revision: D58227612

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codecov-commenter commented Jun 13, 2024

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 95.21%. Comparing base (1139ea0) to head (9f8e4bc).

Additional details and impacted files
@@           Coverage Diff           @@
##             main    #2517   +/-   ##
=======================================
  Coverage   95.21%   95.21%           
=======================================
  Files         485      485           
  Lines       47238    47256   +18     
=======================================
+ Hits        44978    44996   +18     
  Misses       2260     2260           

☔ View full report in Codecov by Sentry.
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This pull request was exported from Phabricator. Differential Revision: D58227612

sdaulton added a commit to sdaulton/Ax-1 that referenced this pull request Jun 13, 2024
…ok#2517)

Summary:
Pull Request resolved: facebook#2517

see title. This change is particularly important for model selection using the NLL if we have noisy observations. Using the posterior over the true function and not the noisy observations gives quite misleading results about model calibration.

I also think that predicted vs actual plots from LOOCV are insightful when using the posterior predictive when the observations are noisy. We may want to consider adding observation_noise to `predict`, but we can do that in a follow-up.

Differential Revision: D58227612
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This pull request was exported from Phabricator. Differential Revision: D58227612

sdaulton added a commit to sdaulton/Ax-1 that referenced this pull request Jun 13, 2024
…ok#2517)

Summary:
Pull Request resolved: facebook#2517

see title. This change is particularly important for model selection using the NLL if we have noisy observations. Using the posterior over the true function and not the noisy observations gives quite misleading results about model calibration.

I also think that predicted vs actual plots from LOOCV are insightful when using the posterior predictive when the observations are noisy. We may want to consider adding observation_noise to `predict`, but we can do that in a follow-up.

Reviewed By: Balandat

Differential Revision: D58227612
…ok#2517)

Summary:
Pull Request resolved: facebook#2517

see title. This change is particularly important for model selection using the NLL if we have noisy observations. Using the posterior over the true function and not the noisy observations gives quite misleading results about model calibration.

I also think that predicted vs actual plots from LOOCV are insightful when using the posterior predictive when the observations are noisy. We may want to consider adding observation_noise to `predict`, but we can do that in a follow-up.

Reviewed By: Balandat

Differential Revision: D58227612
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This pull request was exported from Phabricator. Differential Revision: D58227612

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This pull request has been merged in 4ef840b.

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