Extends the CRP to a Pitman-Yor Process and uses Loom-style hyper-parameter grids for inference; using and modifying code from this branch but ensuring all tests pass again.
Why do we want this?
We're using CGPM in combination with Loom for structure learning. Here we use for initial inference CGPM for clean-up with fully Bayesian inference. Currently, that process is wasteful because we discard all hyper-parameters.
How was this tested?
The test suite was modified so that all CRP tests pass for Pitman-Yor with discount parameter = 0.
fsaad
commented
4 months ago
What does this do?
Extends the CRP to a Pitman-Yor Process and uses Loom-style hyper-parameter grids for inference; using and modifying code from this branch but ensuring all tests pass again.
Why do we want this?
We're using CGPM in combination with Loom for structure learning. Here we use for initial inference CGPM for clean-up with fully Bayesian inference. Currently, that process is wasteful because we discard all hyper-parameters.
How was this tested?
The test suite was modified so that all CRP tests pass for Pitman-Yor with discount parameter = 0.
This PR is best-reviewed commit-by-commit.