Improve Bayesian posterior displays and DREAM CLI robustness#174
Merged
Conversation
AndrewSazonov
added
the
[scope] enhancement
Codecov Report❌ Patch coverage is Additional details and impacted files@@ Coverage Diff @@
## develop #174 +/- ##
===========================================
+ Coverage 86.37% 86.43% +0.06%
===========================================
Files 159 159
Lines 12451 12549 +98
Branches 1442 1463 +21
===========================================
+ Hits 10754 10847 +93
- Misses 1176 1177 +1
- Partials 521 525 +4
Flags with carried forward coverage won't be shown. Click here to find out more.
🚀 New features to boost your workflow:
|
This file contains hidden or 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
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
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Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be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.
Adds ASCII posterior distribution/predictive displays, terminal-width ASCII plot resampling, and no-arg posterior distribution plotting for all free parameters. Improves posterior pair marginals by using full samples, guards DREAM multiprocessing in spawn-based direct scripts, removes unused dependencies, and updates Bayesian docs/tutorials.