{
  "abstract": "Bayesian computation often scales poorly with increasing data size, motivating developments such as divide-and-conquer approaches for scalable inference. These methods partition the data into subsets, perform parallel inference on each subset, and aggregate the results into a single posterior. Appealing theoretical properties and practical performance have been demonstrated for independent data; however, methods for dependent data remain challenging. Existing methods rely on ad hoc approximations with limited theoretical guarantees that lead to potentially poor accuracy in practice. Here, we focus on time-series data and propose a simple, scalable divide-and-conquer method for dependent time series, with theoretically rigorous accuracy guarantees. Simulation studies and real-data examples are used to empirically verify the effectiveness of our approach.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Statistical Science, Duke University",
    "Duke University#TAB#",
    "School of Mathematics and Statistics, University of New South Wales",
    "School of Physics, Mathematics and Computation, The University of Western Australia"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Rihui Ou"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Lachlan Astfalck",
      "orcid": "0000-0002-8369-9384"
    },
    {
      "affs": [
        0
      ],
      "name": "Deborshee Sen",
      "orcid": "0000-0001-9590-5416"
    },
    {
      "affs": [
        0
      ],
      "name": "David B. Dunson",
      "orcid": "0000-0003-4942-1597"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/19spgg0jbr1nw.bib",
  "cited_by": 6,
  "doi": "10.1080/01621459.2026.2688950",
  "fulltext_url": null,
  "id": "19spgg0jbr1nw",
  "issns": [
    "0162-1459",
    "1537-274X"
  ],
  "landing_url": "https://doi.org/10.1080/01621459.2026.2688950",
  "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": "W3177266934",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/19spgg0jbr1nw.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T13:21:00Z",
      "native_id": "W3177266934",
      "source": "openalex",
      "url": "https://doi.org/10.1080/01621459.2026.2688950"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "openalex:2613|Markov Chains and Monte Carlo Methods",
      "subfield": "statistics"
    }
  ],
  "title": "Scalable Bayesian Inference for Time Series via Divide and Conquer",
  "title_text": "Scalable Bayesian Inference for Time Series via Divide and Conquer",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T13:20:22Z",
  "url": "https://research.draconitic.com/paper/19spgg0jbr1nw",
  "venue": "Journal of the American Statistical Association",
  "versions": []
}