Pith Number

pith:RLHFK7OV

pith:2026:RLHFK7OVLTDLG36FTD2SUTGXSC

not attested not anchored not stored refs resolved

Multi-Marginal Couplings for Metropolis-Hastings

Ashish Khisti, Buu Phan, Gergely Flamich, Shahab Asoodeh

Multi-marginal couplings with adaptive Poisson Monte Carlo reduce meeting times for multiple Metropolis-Hastings chains by up to 50 percent.

arxiv:2605.12807 v1 · 2026-05-12 · stat.CO · cs.IT · math.IT

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Record completeness

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

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Claims

C1strongest claim

Experiments on grand couplings of Markov chains show that our methods improve coalescence rates across dimensions, reducing meeting times by up to 50% compared with existing baselines.

C2weakest assumption

The adaptive rule for updating the point process preserves the validity of the multi-marginal coupling while removing the dimension-dependent runtime bottleneck.

C3one line summary

Multi-marginal couplings combined with an adaptive shared-randomness Poisson Monte Carlo method improve coalescence rates for multiple Metropolis-Hastings chains, cutting meeting times by up to 50%.

References

29 extracted · 29 resolved · 1 Pith anchors

[1] Pairwise optimal coupling of multiple random variables.arXiv preprint arXiv:1903.00632, 2019 1903

[2] Estimating convergence of markov chains with l-lag couplings.Advances in neural information processing systems, 32, 2019 2019

[3] General methods for monitoring convergence of iterative simulations.Journal of computational and graphical statistics, 7(4):434–455, 1998 1998

[4] A coupling-based approach to f-divergences diagnostics for markov chain monte carlo.arXiv preprint arXiv:2510.07559, 2025 2025

[5] Coupling without communi- cation and drafter-invariant speculative decoding 2025

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T03:09:12.579734Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

8ace557dd55cc6b36fc598f52a4cd790806bbd8b47d807be369ecd22920e4539

Aliases

arxiv: 2605.12807 · arxiv_version: 2605.12807v1 · doi: 10.48550/arxiv.2605.12807 · pith_short_12: RLHFK7OVLTDL · pith_short_16: RLHFK7OVLTDLG36F · pith_short_8: RLHFK7OV

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/RLHFK7OVLTDLG36FTD2SUTGXSC \
  | 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: 8ace557dd55cc6b36fc598f52a4cd790806bbd8b47d807be369ecd22920e4539

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "d098f7e283f2f152065047f75312a62ae3ab4703f3360f008609b1c971d3de8b",
    "cross_cats_sorted": [
      "cs.IT",
      "math.IT"
    ],
    "license": "http://creativecommons.org/licenses/by-sa/4.0/",
    "primary_cat": "stat.CO",
    "submitted_at": "2026-05-12T22:59:47Z",
    "title_canon_sha256": "d3f052aa21bdbdbd1d180a316c32be343642c57b629ba045889ed8cbca376c7a"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.12807",
    "kind": "arxiv",
    "version": 1
  }
}