weibull regression (MAP and bayesian version) #6
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
1 similar comment
|
will repurpose this to a straightforward MAP using scipy.optimize |
erikbern
changed the title
|
merging this for now since it doesn't interfere with any of the existing code kind of annoying to have three different versions of the same tool though, i might unify it later |
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Outdated suggestions cannot be applied.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.
Implemented a regression model using pymc3, so fully Bayesian.
The good news is that it was very easy to implement (once I discovered the pymc3.Potential class). The bad news is this is slow as hell. It barely works for more than a few thousand datapoints – whereas the current models happily fit 100k datapoints in a few seconds.
I'm tempted to revert to my original idea of just fitting this using MAP and then computing the Hessian of the MAP to get a normal approximation of the posterior distribution. Seems a bit janky but I think it will be a pretty good approximation in practice, and probably ~100x faster.