Pith Number

pith:APRNZNKO

pith:2026:APRNZNKOKPDRIB5JEZ4TLCL3CY

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Stabilised weighted data subsampling for accelerated inference in models with recursive likelihoods

Aishwarya Bhaskaran, Matias Quiroz, Thomas Goodwin, Zixuan Wang

Stabilised weighted subsampling yields unbiased log-likelihood estimates for faster inference in recursive models.

arxiv:2605.13397 v1 · 2026-05-13 · stat.ME · stat.CO

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Claims

C1strongest claim

The proposed estimators are generic building blocks for subsampling-based inference and can be embedded within frameworks including stochastic optimisation, variational Bayes, and Markov chain Monte Carlo. Applications to conditional volatility models, including standard and threshold generalised autoregressive conditional heteroskedasticity models, demonstrate substantial computational speed-ups while maintaining inferential accuracy.

C2weakest assumption

That a stabilisation framework exists which restricts sampling-probability decay to simultaneously avoid both high estimator variance and high computational cost, and that this can be achieved through principled hyperparameter tuning without introducing bias.

C3one line summary

Stabilised weighted subsampling yields an unbiased log-likelihood estimator for recursive models that reduces recursion depth and computational cost while avoiding variance inflation via principled decay restrictions.

References

64 extracted · 64 resolved · 1 Pith anchors

[1] Ai, M., Yu, J., Zhang, H., and Wang, H. (2021). Optimal subsampling algorithms for big data regressions. Statistica Sinica , 31(2):749--772 2021

[2] Aicher, C., Putcha, S., Nemeth, C., Fearnhead, P., and Fox, E. (2025). Stochastic gradient MCMC for nonlinear state space models. Bayesian Analysis , 20(1):83 -- 105 2025

[3] Amari, S.-i. (1998). Natural gradient works efficiently in learning. Neural Computation , 10(2):251--276 1998

[4] Bardenet, R., Doucet, A., and Holmes, C. (2014). Towards scaling up M arkov chain M onte C arlo: A n adaptive subsampling approach. Proceedings of the 31st International Conference on Machine Learning 2014

[5] Bardenet, R., Doucet, A., and Holmes, C. (2017). On M arkov chain M onte C arlo methods for tall data. Journal of Machine Learning Research , 18(47):1--43 2017

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T02:44:47.630996Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

03e2dcb54e53c71407a9267935897b162e62c0fc611667a4122c999a550cea4f

Aliases

arxiv: 2605.13397 · arxiv_version: 2605.13397v1 · doi: 10.48550/arxiv.2605.13397 · pith_short_12: APRNZNKOKPDR · pith_short_16: APRNZNKOKPDRIB5J · pith_short_8: APRNZNKO

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/APRNZNKOKPDRIB5JEZ4TLCL3CY \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 03e2dcb54e53c71407a9267935897b162e62c0fc611667a4122c999a550cea4f

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "e09f11e9719702522e093aa0c34559ff5de51b203b7a999394575533e8171ebf",
    "cross_cats_sorted": [
      "stat.CO"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "stat.ME",
    "submitted_at": "2026-05-13T11:53:57Z",
    "title_canon_sha256": "6d785c1426a11ec842ba2709c6176cd083ea7aec096088f57d1adbfd1a3cefe4"
  },
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  "source": {
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    "kind": "arxiv",
    "version": 1
  }
}