Pith Number

pith:R4LW7JVF

pith:2026:R4LW7JVFICMEXY6J77WRYNMTXF

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Coupling-Informed Transport Maps for Bayesian Filtering in Nonlinear Dynamical Systems

Dengfei Zeng, Dunhui Xiao, Lijian Jiang, Shuyu Sun

Coupling-informed transport maps approximate non-Gaussian posteriors in Bayesian filtering by minimizing MMD via gradient flows, with convergence analysis and high-dimensional localization.

arxiv:2605.13174 v1 · 2026-05-13 · stat.ML · cs.LG · stat.CO

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Claims

C1strongest claim

The proposed approach accurately approximates non-Gaussian filtering posteriors and avoids particle collapse. We provide a convergence analysis for the expectation of the MMD between the approximated posterior and the truth posterior.

C2weakest assumption

The block-triangular structure in the transport map based on couplings between state and observation variables allows reformulation as MMD minimization, and gradient flows yield an analytic transport map implying the steepest descent direction.

C3one line summary

Coupling-informed transport maps approximate non-Gaussian posteriors in Bayesian filtering by minimizing MMD via gradient flows, with convergence analysis and high-dimensional localization.

References

35 extracted · 35 resolved · 0 Pith anchors

[1] SIAM Journal on Scientific Computing , volume =

[2] Proceedings of Machine Learning Research , pages =

[3] Arbel, Michael and Korba, Anna and Salim, Adil and Gretton, Arthur , year = 2019, booktitle =. Maximum 2019

[4] Data Assimilation:

[5] and Marzouk, Youssef M 2024

Receipt and verification

First computed	2026-05-18T03:08:56.498109Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

8f176fa6a540984be3c9ffed1c3593b963e08016792bae73c85b469901ce5b28

Aliases

arxiv: 2605.13174 · arxiv_version: 2605.13174v1 · doi: 10.48550/arxiv.2605.13174 · pith_short_12: R4LW7JVFICME · pith_short_16: R4LW7JVFICMEXY6J · pith_short_8: R4LW7JVF

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/R4LW7JVFICMEXY6J77WRYNMTXF \
  | 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: 8f176fa6a540984be3c9ffed1c3593b963e08016792bae73c85b469901ce5b28

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "264cbda82d5ebece2732453aea4b5a146805958975266f326bac031f8816fb3b",
    "cross_cats_sorted": [
      "cs.LG",
      "stat.CO"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "stat.ML",
    "submitted_at": "2026-05-13T08:36:49Z",
    "title_canon_sha256": "b6db7a6514ab0f3fe2265c552f9d4bcf7144a41b0c9a152f6008653dd7306edf"
  },
  "schema_version": "1.0",
  "source": {
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    "kind": "arxiv",
    "version": 1
  }
}