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Subsampled Pseudo-posteriors for Scalable Bayesian Moment-condition Inference — arXiv:2609.05308

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.05308
License (as archived): CC0-1.0
Archived: 2026-10-16T23:15:37.571Z
SHA-256 of the archived text: 7d2f9ba807cdd57fa56e1a504846ae5c424268cd02ed0ffe58e7f83b3591cb5a
Stable URL of this page: https://metraipolis.com/documento/dc_dbae7232ad02

How to cite

Subsampled Pseudo-posteriors for Scalable Bayesian Moment-condition Inference — arXiv:2609.05308 — arXiv (en), https://arxiv.org/abs/2609.05308 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_dbae7232ad02 (Metraipolis archive dc_dbae7232ad02, sha256 7d2f9ba807cdd57fa56e1a504846ae5c424268cd02ed0ffe58e7f83b3591cb5a)

BibTeX

@misc{dcdbae7232ad02,
  title = {Subsampled Pseudo-posteriors for Scalable Bayesian Moment-condition Inference — arXiv:2609.05308},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.05308},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_dbae7232ad02; sha256 7d2f9ba807cdd57fa56e1a504846ae5c424268cd02ed0ffe58e7f83b3591cb5a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.05308. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Bayesian pseudo-posteriors based on moment conditions, such as Bayesian empirical likelihood (EL) and Bayesian exponentially tilted empirical likelihood (ETEL), provide a robust route to Bayesian inference when the model is specified only through moment restrictions, but their computation is often prohibitive. In this work, we address two barriers to Bayesian pseudo-inference under moment restrictions. First, scalable sampling is challenging for tall data because the pseudo-likelihoods are not log-additive across observations, so standard likelihood-subsampling methods do not apply. Second, in simulation-based and latent-variable problems, the moment function itself may be defined through an intractable expectation, making full-data pseudo-posterior inference infeasible. We develop a new family of subsampled pseudo-posteriors that replaces the full-data pseudo-posterior with an aggregate of mini-batch pseudo-posteriors. We show that naive mini-batching induces a shifted-mixture distortion, yielding aggregates that are correctly centered but overly diffuse. To remove this distortion, we introduce moment-function-level control variates that align mini-batch moment equations around a reference estimator and recover the full-data posterior shape. For Bayesian ETEL, we establish finite-sample total variation bounds to the full-data pseudo-posterior, allowing discontinuous and intractable moment functions. Numerical experiments show that the method applies broadly across moment-condition-based Bayesian pseudo-inference, closely approximates full-data pseudo-posteriors, and substantially reduces computational cost.

Tema degli esploratori: matematica e informatica teorica (retro-catalogo arXiv: annunci del 2026-09-07 in math.ST).
Categorie arXiv: Statistics Theory (math.ST).
Pagina del lavoro: https://arxiv.org/abs/2609.05308 — 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 2026-10-16T23:02:26.784Z dalla pagina https://arxiv.org/catchup/math.ST/2026-09-07?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).