Allow for a GP to model covariance matrix

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
25/100
Issue type
Feature
Clarity
Needs clarification
Activity status
Stale
Tech stack
python
Domain
machine-learning

Research direction

Start by locating the code that computes returned posteriors and handles uncertainties, then review how covariance and GP hyper-parameters are represented. Done means implementing the requested GP covariance model and checking whether marginalizing its hyper-parameters improves the returned posteriors.

Written by the indexing model from the issue text.

Description

Posteriors are wildly underestimated, likely due to the poor (non-existent) handling of uncertainties. Try and implement a GP to model the covariance matrix and then marginalize over its hyper-parameters to see if that improves the returned posteriors.

Dominant language
Python
Stars
23
Forks
10
Avg merge
28m
Merged PRs (30d)
1

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