# Accelerated Bayesian parameter estimation and model selection for gravitational waves with normalizing flows

arXiv: https://arxiv.org/abs/2410.21076 · HTML: https://mlsanity.com/paper/2410.21076/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 53 references that are in the map.
- First posted on arXiv 28 Oct 2024.

## Builds on (most cited first, 18 of 18)

- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Normalizing Flows for Probabilistic Modeling and Inference](https://mlsanity.com/paper/1912.02762/) (2019)
- [Neural Spline Flows](https://mlsanity.com/paper/1906.04032/) (2019)
- [A Conceptual Introduction to Hamiltonian Monte Carlo](https://mlsanity.com/paper/1701.02434/) (2017)
- [Real-time gravitational-wave science with neural posterior estimation](https://mlsanity.com/paper/2106.12594/) (2021)
- [Latent Normalizing Flows for Discrete Sequences](https://mlsanity.com/paper/1901.10548/) (2019)
- [Resampling Base Distributions of Normalizing Flows](https://mlsanity.com/paper/2110.15828/) (2021)
- [Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy](https://mlsanity.com/paper/1909.06296/) (2019)
- [Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference](https://mlsanity.com/paper/2210.05686/) (2022)
- [Complete parameter inference for GW150914 using deep learning](https://mlsanity.com/paper/2008.03312/) (2020)
- [Gravitational-wave parameter estimation with autoregressive neural network flows](https://mlsanity.com/paper/2002.07656/) (2020)
- [A RAD approach to deep mixture models](https://mlsanity.com/paper/1903.07714/) (2019)
- [Learning Bayesian posteriors with neural networks for gravitational-wave inference](https://mlsanity.com/paper/1909.05966/) (2019)
- [Machine learning assisted Bayesian model comparison: learnt harmonic mean estimator](https://mlsanity.com/paper/2111.12720/) (2021)
- [Learned harmonic mean estimation of the Bayesian evidence with normalizing flows](https://mlsanity.com/paper/2405.05969/) (2024)
- [Normalizing Constant Estimation with Gaussianized Bridge Sampling](https://mlsanity.com/paper/1912.06073/) (2019)
- [Swift sky localization of gravitational waves using deep learning seeded importance sampling](https://mlsanity.com/paper/2111.00833/) (2021)
- [The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparison](https://mlsanity.com/paper/2405.12965/) (2024)

## Built on by (most cited first, 1 of 1)

- [Learned harmonic mean estimation of the marginal likelihood for multimodal posteriors with flow matching](https://mlsanity.com/paper/2601.18683/) (2026)

## Abstract (arXiv)

We present an accelerated pipeline, based on high-performance computing techniques and normalizing flows, for joint Bayesian parameter estimation and model selection and demonstrate its efficiency in gravitational wave astrophysics. We integrate the Jim inference toolkit, a normalizing flow-enhanced Markov chain Monte Carlo (MCMC) sampler, with the learned harmonic mean estimator. Our Bayesian evidence estimates run on 1 GPU are consistent with traditional nested sampling techniques run on 16 CPU cores, while reducing the computation time by factors of 5× and 15× for 4-dimensional and 11-dimensional gravitational wave inference problems, respectively. Our code is available in well-tested and thoroughly documented open-source packages, ensuring accessibility and reproducibility for the wider research community.