mlsanityData to 14 Oct 2026

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

arXiv 2410.21076 ↗First posted 28 Oct 2024astro-ph.IM astro-ph.HE cs.LGcited by 1

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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.

Key facts. Cited by 1 paper 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.

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18 of 53 references.

References · 18

  1. 2016Density estimation using Real NVPcited by 2,042
  2. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,323
  3. 2019Neural Spline Flowscited by 596
  4. 2017A Conceptual Introduction to Hamiltonian Monte Carlocited by 209
  5. 2021Real-time gravitational-wave science with neural posterior estimationcited by 78
  6. 2019Latent Normalizing Flows for Discrete Sequencescited by 67
  7. 2021Resampling Base Distributions of Normalizing Flowscited by 42
  8. 2019Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomycited by 36
  9. 2022Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inferencecited by 28
  10. 2020Complete parameter inference for GW150914 using deep learningcited by 25
  11. 2020Gravitational-wave parameter estimation with autoregressive neural network flowscited by 19
  12. 2019A RAD approach to deep mixture modelscited by 18
6 more
  1. 2019Learning Bayesian posteriors with neural networks for gravitational-wave inferencecited by 18
  2. 2021Machine learning assisted Bayesian model comparison: learnt harmonic mean estimatorcited by 9
  3. 2024Learned harmonic mean estimation of the Bayesian evidence with normalizing flowscited by 7
  4. 2019Normalizing Constant Estimation with Gaussianized Bridge Samplingcited by 6
  5. 2021Swift sky localization of gravitational waves using deep learning seeded importance samplingcited by 4
  6. 2024The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparisoncited by 3

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Citing papers per year: 2026: 1.

1 paper.

  1. 2026Learned harmonic mean estimation of the marginal likelihood for multimodal posteriors with flow matchingcited by 0