Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Consistent sampling features across methods #274

Open
jdherman opened this issue Nov 7, 2019 · 0 comments
Open

Consistent sampling features across methods #274

jdherman opened this issue Nov 7, 2019 · 0 comments

Comments

@jdherman
Copy link
Member

jdherman commented Nov 7, 2019

There are many nice sampling features, but they are not always implemented consistently across methods.

  • Group sampling:
    Currently works for Sobol and Morris. Is it possible to implement for other methods?

  • Nonuniform sampling:
    Currently works for Sobol, with several others in progress Feature - non uniform sampling - DNM #192
    The distributions are uniform, triangular, normal, and lognormal. Consider others? Maybe the option to pass any CDF function from scipy into the sampler. That could remove the confusing bounds parameter, where the parameters for the other distributions are not really bounds.

Building on the ideas in #234 , it might be possible to refactor these sampling features to avoid duplicate code and improve compatibility across methods.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Projects
None yet
Development

No branches or pull requests

1 participant