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Amortized ratio-estimation importance sampling and localized simulation-based calibration for intractable likelihoods — arXiv:2609.39712

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.39712
License (as archived): CC0-1.0
Archived: 2026-10-07T14:09:11.961Z
SHA-256 of the archived text: 40648676a715eca0e9fdb2fe58cf8c1502b0a6f8946b28dad6fab8dc3ce2fa55
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How to cite

Amortized ratio-estimation importance sampling and localized simulation-based calibration for intractable likelihoods — arXiv:2609.39712 — arXiv (en), https://arxiv.org/abs/2609.39712 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_e467bc2d2f99 (Metraipolis archive dc_e467bc2d2f99, sha256 40648676a715eca0e9fdb2fe58cf8c1502b0a6f8946b28dad6fab8dc3ce2fa55)

BibTeX

@misc{dce467bc2d2f99,
  title = {Amortized ratio-estimation importance sampling and localized simulation-based calibration for intractable likelihoods — arXiv:2609.39712},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.39712},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_e467bc2d2f99; sha256 40648676a715eca0e9fdb2fe58cf8c1502b0a6f8946b28dad6fab8dc3ce2fa55},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.39712. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We consider simulation-based Bayesian inference (SBI) for the parameters of models with intractable likelihoods but tractable forward simulation. Building on Gaussian mixtures-of-experts surrogates, as a first contribution we develop a sequential procedure for posterior estimation in which progressively localized, data informed, conditional density approximations are used as proposal distributions. A final surrogate of the posterior distribution is then corrected using an importance sampling step that introduces a locally amortized likelihood-to-evidence ratio estimator. A second contribution is localized simulation-based calibration (SBC). Localized SBC probe calibration over neighborhoods that are deliberately broader than the available posterior, at low additional computational cost. Rather than repeatedly running the full inference procedure for each simulated pseudo-observation, localized SBC fits the local surrogate and ratio estimator only once for each local design, and reuses them across all pseudo-observations. This approach is particularly appealing for SBI applications. We evaluate both contributions on three case studies, including a real-data epidemiological application.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-10-01 in stat.CO).
Categorie arXiv: Methodology (stat.ME) ; Computation (stat.CO); Machine Learning (stat.ML).
Pagina del lavoro: https://arxiv.org/abs/2609.39712 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T14:02:43.257Z dalla pagina https://arxiv.org/catchup/stat.CO/2026-10-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).