{
  "abstract": "Slice sampling is a Markov chain Monte Carlo algorithm that draws its next state uniformly from a \"slice\"---a super-level set of the target density function---at each iteration, thereby providing automatic local adaptivity to the scale of the target. In practice the exact slice is not known, so general-purpose implementations use an approximate slice that is grown from a starting interval of length $w>0$, with a computational cost that depends on $w$. This work presents an analysis of the average per-iteration number of target density evaluations, as a function of $w$, of hybrid slice sampling with various slice-finding schemes for targets with contiguous slices. The paper uses the results of the analysis to develop automated, slice-adaptive tuning schemes along with suboptimality bounds and asymptotic convergence guarantees. Simulations demonstrate that the tuning schemes reliably yield near-optimal slice-adaptive tuning with essentially no dependence on the initial setting of $w$.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.08172",
  "author_total": 1,
  "authors": [
    {
      "name": "Trevor Campbell"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/57825p7vryqjd.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.08172",
  "fulltext_url": null,
  "id": "57825p7vryqjd",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.08172",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.CO"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "2613|Markov Chains and Monte Carlo Methods"
    ]
  ],
  "openalex": "W7212026139",
  "pdf_url": "https://arxiv.org/pdf/2609.08172",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/57825p7vryqjd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:07Z",
      "native_id": "2609.08172",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.08172"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.CO",
      "subfield": "statistics"
    }
  ],
  "title": "Optimal Slice-Adaptive Tuning of Hybrid Slice Sampling",
  "title_text": "Optimal Slice-Adaptive Tuning of Hybrid Slice Sampling",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:07Z",
  "url": "https://research.draconitic.com/paper/57825p7vryqjd",
  "venue": "arXiv",
  "versions": []
}