-
Notifications
You must be signed in to change notification settings - Fork 141
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
Implementation of the power spectrum inference with GPs #832
base: main
Are you sure you want to change the base?
Conversation
Hello @mlefkir! Thanks for updating this PR. We checked the lines you've touched for PEP 8 issues, and found:
Comment last updated at 2024-10-03 11:36:06 UTC |
@mlefkir thanks for your contribution to Stingray, and sorry for my late reply! From the point of view of the requirements for PRs to Stingray:
|
Yes, @mlefkir, do you have any use case example for this method (Something like a .ipynb notebook). Also @dhuppenkothen, can you also have a look into the usefulness of this method for Stingray, and whether it should go into the same file as the gpmodeling part? |
@Gaurav17Joshi @matteobachetti I made two examples available here Examples, one uses nested sampling with the jaxns sampler already called in Stingray and the other one uses NUTS with NumPyro. |
@mlefkir I'm playing with your PR. Really sorry for the slow progress, but my knowledge of these methods is pretty poor and it takes me a lot of time to just understand how it works, and... my agenda is pretty full 😅 . |
This is the Python-JAX implementation of a method to infer the power spectral density of irregular time series using Gaussian process regression. The method is described in a forthcoming paper and in the Julia package Pioran.jl, it relies on approximating a bending power-law model in a sum of scalable kernels implemented in tinygp.
Relevant Issue(s)/PR(s)
Provide an overview of the implemented solution or the fix and elaborate on the modifications.
Is there a new dependency introduced by your contribution? If so, please specify.
Any other comments?