Improve factor analysis notebook #612
Merged
Conversation
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
|
Check out this pull request on See visual diffs & provide feedback on Jupyter Notebooks. Powered by ReviewNB |
erik-werner
force-pushed
the
fix_factor_analysis
branch
from
fc117b8 to
3af4f51
Compare
twiecki
reviewed
Nov 29, 2023
Also add short sampling of amortized model, to show that it works but is slow.
Positive-only works for the particular data we have here, where the latent factors are defined as positive. But it makes it impossible to fit models without this constraint.
erik-werner
changed the title
twiecki
approved these changes
Dec 28, 2023
|
Thanks! |
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Outdated suggestions cannot be applied.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.
Fixes #587
Updating factor analysis notebook to
Fix some small errors in the mathematical derivations and related code
Spell out some steps in the derivations that were not obvious (to me at least)
Replace the least-squares derivation of$F$ by a Bayesian derivation
Add a section at the end that compares the factors we have identified to the true ones for the synthetic data we have used
Do some minor edits to the text, in particular to fit the text to the updated sampling results
Notebook follows style guide https://docs.pymc.io/en/latest/contributing/jupyter_style.html
PR description contains a link to the relevant issue:
Check the notebook is not excluded from any pre-commit check: https://github.com/pymc-devs/pymc-examples/blob/main/.pre-commit-config.yaml
📚 Documentation preview 📚: https://pymc-examples--612.org.readthedocs.build/en/612/