SGVB PSD Estimator
This repository contains the code for the papers
- "Variational inference for correlated gravitational wave detector network noise" by Jianan Liu et al. 2024
- "Variational Bayesian Inference for the Spectral Structure of LISA Noise" (arXiv link to be added)
Documentation is available at https://nz-gravity.github.io/sgvb_psd/
Inference modes
Version 2.0.0 keeps the original non-eigenbasis joint SGVB method as the default and adds joint and factorized eigenbasis methods.
from sgvb_psd.psd_estimator import PSDEstimator
# Original joint SGVB with the blocked Whittle likelihood (default)
legacy = PSDEstimator(x=data)
# Joint SGVB with the blocked eigenbasis likelihood
eigen_joint = PSDEstimator(
x=data,
use_eigenbasis=True,
posterior_mode="joint",
)
# Factorized (componentwise) SGVB with the blocked eigenbasis likelihood
eigen_factorized = PSDEstimator(
x=data,
use_eigenbasis=True,
posterior_mode="factorized",
)
Development
Install in editable mode with dev dependencies
pip install -e ".[dev]"
pre-commit install
Ensure unit tests are passing locally and on the CI!
pytest tests/
1 maintainer
Metadata
1 maintainer
Release files for sgvb-psd 2.0.0
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