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Source Distribution Estimation by Posterior Averaging — arXiv:2609.02622

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.02622
License (as archived): CC0-1.0
Archived: 2026-10-17T05:00:51.501Z
SHA-256 of the archived text: a7d7c4e5f67c7d4cc9f725b8523f08e1b2df0ee2cca7d21ecc401b667e25d00e
Stable URL of this page: https://metraipolis.com/documento/dc_cd81058ea1fd

How to cite

Source Distribution Estimation by Posterior Averaging — arXiv:2609.02622 — arXiv (en), https://arxiv.org/abs/2609.02622 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cd81058ea1fd (Metraipolis archive dc_cd81058ea1fd, sha256 a7d7c4e5f67c7d4cc9f725b8523f08e1b2df0ee2cca7d21ecc401b667e25d00e)

BibTeX

@misc{dccd81058ea1fd,
  title = {Source Distribution Estimation by Posterior Averaging — arXiv:2609.02622},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.02622},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cd81058ea1fd; sha256 a7d7c4e5f67c7d4cc9f725b8523f08e1b2df0ee2cca7d21ecc401b667e25d00e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.02622. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Simulation-based science often requires a distribution over simulator parameters whose push-forward reproduces a set of real observations: this is the source distribution estimation (SDE) problem. Existing methods fit the source against a likelihood surrogate trained once from a fixed proposal prior. Their objective is therefore stated only in terms of the surrogate instead of the true simulator, which may fail for inaccurate areas in parameter space where the surrogate was never trained. We instead solve SDE by expectation maximization: an E-step trains an amortized posterior on fresh simulations from the current source estimate, and an M-step refits the source to the average of that posterior over the observed data. We give two parameterizations, (1) separate source and posterior flows and (2) a single shared conditional flow. We evaluate our method on three benchmark tasks under both broad and misspecified initial priors. Both improve on existing fixed surrogate approaches and on iterated variants of each, most clearly on Lotka--Volterra, where no baseline falls below 0.96 data-space C2ST while our methods reach 0.64-0.68 in three of four initial-prior settings.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-03 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.02622 — 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 2026-10-17T04:09:43.331Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-03?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).