Pith Number

pith:SLOQUP34

pith:2026:SLOQUP34GUNX5IHU53ELKW63HD

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A Bayesian Adaptive Latent Mixture Model for Zero-Inflated Weighted Brain Connectome Analysis

Hsin-Hsiung Huang, Teng Zhang, Yuh-Haur Chen

A Bayesian mixture model represents each brain network as a simplex mixture of shared low-rank latent templates while separating edge presence from strength.

arxiv:2605.12901 v1 · 2026-05-13 · stat.ME · stat.AP · stat.CO

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Claims

C1strongest claim

The model recovers stable latent score patterns and heterogeneous subject-level mixtures in Human Connectome Project data; posterior consistency, local asymptotic normality, Bernstein-von Mises approximation, and predictive consistency hold for an identifiable quotient-space estimand under fixed-template scenario.

C2weakest assumption

That subject networks are well-represented as simplex mixtures of a small number of shared low-rank latent score matrices, with the sparsity-coupling parameter correctly capturing dependence between absent edges and latent structure, and that template count selection via predictive fit yields an identifiable model.

C3one line summary

A Bayesian adaptive latent mixture model using simplex mixtures of low-rank latent score matrices and hurdle likelihoods for zero-inflated weighted brain connectomes, with posterior consistency and predictive consistency established.

References

46 extracted · 46 resolved · 0 Pith anchors

[1] Journal of Machine Learning Research , year =

[2] Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? , author=. NeuroImage , volume=. 2007 , publisher= 2007

[3] Journal of the American Statistical Association , volume= 2017

[4] Accurate and robust brain image alignment using boundary-based registration , author=. NeuroImage , volume=. 2009 , publisher= 2009

[5] Cai, Yuhua and Owen, Jonathan P. and Eriksson, M. and Reh, G. R. and Martin, L. and Irimia, A. and Davenport, N. D. and Mukherjee, P. and Mayer, A. R. , journal=. 2024 , doi= 2024

Receipt and verification

First computed	2026-05-18T03:09:10.734500Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

92dd0a3f7c351b7ea0f4eec8b55bdb38ea563043e91744b22dd5ee8109ff67de

Aliases

arxiv: 2605.12901 · arxiv_version: 2605.12901v1 · doi: 10.48550/arxiv.2605.12901 · pith_short_12: SLOQUP34GUNX · pith_short_16: SLOQUP34GUNX5IHU · pith_short_8: SLOQUP34

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/SLOQUP34GUNX5IHU53ELKW63HD \
  | 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: 92dd0a3f7c351b7ea0f4eec8b55bdb38ea563043e91744b22dd5ee8109ff67de

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "fda8c4001440f57cbe9a8f61d25cc8caabed4cc26666d2eedeceb9ae7a90bcff",
    "cross_cats_sorted": [
      "stat.AP",
      "stat.CO"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "stat.ME",
    "submitted_at": "2026-05-13T02:25:09Z",
    "title_canon_sha256": "b5c8dba113ed6906ec1511f9090a0742ad2d68a18f6bb974dcbf0ddb96872153"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.12901",
    "kind": "arxiv",
    "version": 1
  }
}