mlsanityData to 7 Oct 2026

Calibrating general posterior credible regions

arXiv 1509.00922 ↗First posted 3 Sep 2015stat.MEcited by 26

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Abstract (arXiv)

An advantage of methods that base inference on a posterior distribution is that credible regions are readily obtained. Except in well-specified situations, however, there is no guarantee that such regions will achieve the nominal frequentist coverage probability, even approximately. To overcome this difficulty, we propose a general strategy that introduces an additional scalar tuning parameter to control the posterior spread, and we develop an algorithm that chooses this parameter so that the corresponding credible region achieves the nominal coverage probability.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Sep 2015.

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Citing papers per year: 2021: 1; 2022: 2; 2023: 7; 2024: 7; 2025: 4; 2026: 5.

26 papers.

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  1. 2021User-friendly introduction to PAC-Bayes boundscited by 234
  2. 2022Robust Neural Posterior Estimation and Statistical Model Criticismcited by 45
  3. 2023Robust and Scalable Bayesian Online Changepoint Detectioncited by 16
  4. 2022Generalised Bayesian Inference for Discrete Intractable Likelihoodcited by 14
  5. 2023Robust and Conjugate Gaussian Process Regressioncited by 14
  6. 2024Concentration of a sparse Bayesian model with Horseshoe prior in estimating high-dimensional precision matrixcited by 8
  7. 2024Concentration properties of fractional posterior in 1-bit matrix completioncited by 7
  8. 2024A sparse PAC-Bayesian approach for high-dimensional quantile predictioncited by 6
  9. 2025AI-Powered Bayesian Inferencecited by 6
  10. 2023Semiparametric inference using fractional posteriorscited by 4
  11. 2023On the Temperature of Bayesian Graph Neural Networks for Conformal Predictioncited by 4
  12. 2023Reproducible Parameter Inference Using Bagged Posteriorscited by 2
14 more
  1. 2024Interventional Processes for Causal Uncertainty Quantificationcited by 2
  2. 2023Sequential Gibbs Posteriors with Applications to Principal Component Analysiscited by 1
  3. 2024Adaptive posterior concentration rates for sparse high-dimensional linear regression with random design and unknown error variancecited by 1
  4. 2024Temperature Optimization for Bayesian Deep Learningcited by 1
  5. 2025Two-sample comparison through additive tree models for density ratioscited by 1
  6. 2026Amortized Simulation-Based Inference in Generalized Bayes via Neural Posterior Estimationcited by 1
  7. 2026Generalized Bayes for Causal Inferencecited by 1
  8. 2023On Exact Bayesian Credible Sets for Classification and Pattern Recognitioncited by 0
  9. 2024Addressing the Inconsistency in Bayesian Deep Learning via Generalized Laplace Approximationcited by 0
  10. 2025Variational empirical Bayes variable selection in high-dimensional logistic regressioncited by 0
  11. 2025Generative Regression with IQ-BARTcited by 0
  12. 2026General Bayesian Policy Learningcited by 0
  13. 2026Variational Learning of Fractional Posteriorscited by 0
  14. 2026Supercharging Bayesian Inference with Reliable AI-Informed Priorscited by 0