Allowing user to keep parameters after likelihood transform #143
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sync jim dev
Removed Unused Variable in RunManager Example
Also isort some import in prior.py
Also rename prior
Also add prior test
Inverse limts the type of transform one can use, And it doesn't seem to have case that will require log_prob on target space
This reverts commit 4978cb1.
…-into-jim-class-from-prior-class Updated jim output
Use uniform in component mass in test
Fix jim output functions
…-into-jim-class-from-prior-class 98 moving naming tracking into jim class from prior class
Taking the absolute value of the Jacobian determinant
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
In this PR, the parameter
parameters_to_keepis added toJimand theHeterodynedTransientLikelihoodFDso that the parameters can be kept after the likelihood transform rather than having all of them dropped.In the current approach, only the parameters after all the sampling inverse transforms, i.e., going back from$\vec{z}$ to $\vec{\theta}_{\rm prior}$ , and before all likelihood transforms. We can further discuss whether that is the best placement for such treatment.