bilby¶
A user-friendly Bayesian inference library.
Bilby provides a simple interface to perform parameter estimation with a wide range of stochastic samplers. It is primarily designed and built for inference of compact binary coalescence events in interferometric data, but it can also be used for more general problems.
Install bilby with pip or conda, along with optional
samplers and gravitational-wave dependencies.
Fit a simple model to data and learn the core concepts: likelihoods, priors, samplers, and results.
Run and edit the bilby examples in your browser, with no installation needed.
Analyse compact binary coalescences in real or simulated gravitational-wave data.
Detailed documentation of every module, class, and function in bilby.
Report issues, propose changes, and help develop bilby.
The code is hosted at https://github.com/bilby-dev/bilby, and you can run and edit the examples in your browser with no installation. If you use bilby in a scientific publication, please cite it.
API reference¶
Compatibility layer for running bilby with different array backends via the Python array API. |
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Core functionality for general Bayesian inference: likelihoods, priors, samplers, and results. |
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Gravitational-wave specific functionality: detectors, waveforms, likelihoods, priors, and conversions. |
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Tools for hierarchical inference of population (hyper-)parameters. |
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Bilby-MCMC, a native Markov-chain Monte Carlo sampler designed for gravitational-wave inference. |