PyPI version

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/

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