JuliaGaussianProcesses / JuliaGaussianProcesses/AbstractGPs.jl
log marginal likelihood objective and posterior GP
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- Dominant language
- Julia
- Stars
- 287
- Forks
- 27
- PR merge metrics
- No merged PRs in 30d
Description
When preparing #240 I stumbled across having to pass both FiniteGP for input features f(x) and targets y to both posterior (for PosteriorGP) and logpdf (for log marginal likelihood). Maybe the answer is no, but should we have something like logpdf(::PosteriorGP)?
Also, is it sufficiently intuitive that you get the log marginal likelihood by calling logpdf? why would calling the probability density function return what we get when we integrate out the uncertainty over the GP?
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading the posterior, logpdf, PosteriorGP, and FiniteGP APIs referenced in the issue, then review the context in #240. Determine whether a logpdf(::PosteriorGP) API and clearer naming or documentation are appropriate. Done means the API direction is agreed and covered by the relevant tests or documentation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100