{
  "abstract": "Latent field models are essential for spatial statistics, but the posterior distribution of the latent field, even with a Gaussian process (GP) prior, is analytically intractable whenever the likelihood is non-Gaussian. Existing approximations are either too slow or too restrictive for large non-Gaussian datasets. We present a scalable variational inference framework that mirrors the sparse nearest-neighbor autoregressive structure of the prior in the variational family, uses normalizing-flow conditionals for expressiveness, and amortizes all variational parameters via a single shared neural conditioner. Doubly stochastic optimization with reduced ancestor sets achieves polylogarithmic complexity per iteration.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.13656",
  "author_total": 2,
  "authors": [
    {
      "name": "Paul F. V. Wiemann"
    },
    {
      "name": "Matthias Katzfuss"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/70vcvhg7e56yg.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.13656",
  "fulltext_url": null,
  "id": "70vcvhg7e56yg",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.13656",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.CO"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1702|Gaussian Processes and Bayesian Inference"
    ]
  ],
  "openalex": "W7221551936",
  "pdf_url": "https://arxiv.org/pdf/2610.13656",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/70vcvhg7e56yg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:28:46Z",
      "native_id": "2610.13656",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.13656"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.CO",
      "subfield": "statistics"
    }
  ],
  "title": "Scalable Sparse Autoregressive Variational Inference for Non-Gaussian Spatial Fields",
  "title_text": "Scalable Sparse Autoregressive Variational Inference for Non-Gaussian Spatial Fields",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:46Z",
  "url": "https://research.draconitic.com/paper/70vcvhg7e56yg",
  "venue": "arXiv",
  "versions": []
}