{
  "abstract": "Uncertainty estimates from fuzzy regression are informative only when the implementation is correct and the comparison protocol makes the sources of variation visible. We revisit a first-order Takagi--Sugeno--Kang (TSK) regression implementation and rebuild its dense, conjugate Bayesian, BIC-based, and rule-level spike-and-slab estimators using corrected code. The corrected pipeline freezes the Gaussian membership partition at training time, uses a scaled conjugate prior, evaluates firing strengths in the log domain, and reports direct posterior-predictive quantiles for Bayesian methods. Across 30 outer 80/20 splits on three targets from two UCI regression datasets, corrected TSK-LS obtains $R^2=0.957$, $0.923$, and $0.786$ on Energy-Heating, Energy-Cooling, and Concrete, respectively; rule-level Gibbs sampling with Bayesian model averaging obtains $0.957$, $0.922$, and $0.784$. We then evaluate the same workflow on a bounded chronological sample of the Building Data Genome 2 meter data that covers 158 buildings across 17 sites. In a common-unit evaluation, every method is scored on the same 47,400 test rows and every trained model uses 94,800 train--validation rows. Persistence has RMSE $27.710$, compared with $29.083$ for both TSK-LS and Bayesian TSK; the pre-specified building-level mean RMSE difference between Bayesian TSK and persistence is $+0.0447$ with a 95% site-cluster bootstrap interval of $[-1.5536,1.5665]$. The Bayesian TSK 90% intervals have PICP $0.980$ and MPIW $116.469$. Taken together, the evidence supports a narrower conclusion: spike-and-slab model averaging is a reproducible implementation option, but it does not provide a demonstrated accuracy or calibration advantage across the tested tabular and building-meter settings.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.01447",
  "author_total": 1,
  "authors": [
    {
      "name": "Zhiren Xiao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2a1njpegk8af2.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.01447",
  "fulltext_url": null,
  "id": "2a1njpegk8af2",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.01447",
  "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.ML"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.01447",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2a1njpegk8af2.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:21:21Z",
      "native_id": "2611.01447",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.01447"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Correctness and Predictive Uncertainty in Bayesian TSK Fuzzy Regression: A Reproducible Evaluation on Tabular and Building-Meter Data",
  "title_text": "Correctness and Predictive Uncertainty in Bayesian TSK Fuzzy Regression: A Reproducible Evaluation on Tabular and Building-Meter Data",
  "type": "preprint",
  "updated_at": "2026-11-04T15:21:15Z",
  "url": "https://research.draconitic.com/paper/2a1njpegk8af2",
  "venue": "arXiv",
  "versions": []
}