{
  "abstract": "Annealed sequential Monte Carlo (ASMC) samplers are special cases of SMC samplers where the sequence of distributions can be embedded in a smooth path of distributions. Using this underlying path and a performance model based on the variance of the normalizing constant estimator, we systematically study dense-schedule limits. From our theory emerges a notion of global barrier, capturing the inherent complexity of normalizing constant approximation under our performance model. We then turn the resulting approximations into surrogate objective functions of algorithm performance, using them to guide method development. This leads to novel adaptive methods, optimized annealed SMC (OASMC), which address practical difficulties inherent in previous adaptive SMC methods. First, our OASMC algorithms are predictable: they produce a sequence of increasingly precise estimates at deterministic, known times. Second, optimized annealed importance sampling (OAIS), a special case of OASMC, enables schedule adaptation at a memory cost constant in the number of particles, requiring significantly less communication. Finally, these characteristics make OAIS highly efficient on GPUs. We provide an open-source, high-performance GPU implementation of our method and demonstrate up to a hundred-fold speed improvement compared to state-of-the-art adaptive AIS methods.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Statistics, University of British Columbia , Vancouver, Canada",
    "Department of Statistics, University of Oxford, Oxford ,"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Saifuddin Syed",
      "orcid": "0000-0002-8499-8255"
    },
    {
      "affs": [
        0
      ],
      "name": "Alexandre Bouchard‐Côté",
      "orcid": "0000-0001-7402-8506"
    },
    {
      "affs": [
        0
      ],
      "name": "Kevin T. Chern"
    },
    {
      "affs": [
        1
      ],
      "name": "Arnaud Doucet B",
      "orcid": "0000-0002-7662-419X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/28gqqnfx96r4q.bib",
  "cited_by": 28,
  "doi": "10.1093/jrsssb/qkag082",
  "fulltext_url": null,
  "id": "28gqqnfx96r4q",
  "issns": [
    "1369-7412",
    "1467-9868"
  ],
  "landing_url": "https://doi.org/10.1093/jrsssb/qkag082",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2613|Markov Chains and Monte Carlo Methods"
    ]
  ],
  "openalex": "W7172388324",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-18",
  "publisher": "Oxford University Press",
  "ris": "https://research.draconitic.com/paper/28gqqnfx96r4q.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T13:20:41Z",
      "native_id": "W7172388324",
      "source": "openalex",
      "url": "https://doi.org/10.1093/jrsssb/qkag082"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "openalex:2613|Markov Chains and Monte Carlo Methods",
      "subfield": "statistics"
    }
  ],
  "title": "Optimized annealed sequential Monte Carlo samplers",
  "title_text": "Optimized annealed sequential Monte Carlo samplers",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:20:59Z",
  "url": "https://research.draconitic.com/paper/28gqqnfx96r4q",
  "venue": "Journal of the Royal Statistical Society Series B (Statistical Methodology)",
  "versions": []
}