ArticleJournal of the American Statistical Association2019
Robust Bayesian inference via coarsening.
Article in Journal of the American Statistical Association, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Collective posterior inference from highly variable empirical replicates.PLoS computational biology · 2026Article
- Robust discovery of mutational signatures using power posteriors.PLoS computational biology · 2026Article
- The evolution of genetic drift over 50,000 generations.bioRxiv : the preprint server for biology · 2026Article
- STable AutoCorrelation Integral Estimator: Robust and Accurate Transport Properties from Molecular Dynamics Simulations.Journal of chemical information and modeling · 2025Article
- BAYESIAN LEARNING OF CLINICALLY MEANINGFUL SEPSIS PHENOTYPES IN NORTHERN TANZANIA.The annals of applied statistics · 2025Article
- Product Centred Dirichlet Processes for Bayesian Multiview Clustering.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2025Article
- Robustifying Likelihoods by Optimistically Re-weighting Data.Journal of the American Statistical Association · 2025Article
- Bayesian Clustering via Fusing of Localized Densities.Journal of the American Statistical Association · 2025Article
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- Coarsened Mixtures of Hierarchical Skew Normal Kernels for Flow and Mass Cytometry Analyses.Bayesian analysis · 2024Article
- A Gibbs Posterior Framework for Fair Clustering.Entropy (Basel, Switzerland) · 2024Article
- Spectral Clustering, Bayesian Spanning Forest, and Forest Process.Journal of the American Statistical Association · 2024Article
- Reproducible parameter inference using bagged posteriors.Electronic journal of statistics · 2024Article
- Avoiding inferior clusterings with misspecified Gaussian mixture models.Scientific reports · 2023Article
- Generalized Bayes approach to inverse problems with model misspecification.Inverse problems · 2023Article
- A generalized Bayes framework for probabilistic clustering.Biometrika · 2023Article
- A Bayesian feature allocation model for identifying cell subpopulations using CyTOF data.Journal of the Royal Statistical Society. Series C, Applied statistics · 2023Article
- Bayesian cluster analysis.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Review
- Escaping The Curse of Dimensionality in Bayesian Model-Based Clustering.Journal of machine learning research : JMLR · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The standard approach to Bayesian inference is based on the assumption that the distribution of the data belongs to the chosen model class. However, even a small violation of this assumption can have a large impact on the outcome of a Bayesian procedure. We introduce a novel approach to Bayesian inference that improves robustness to small departures from the model: rather than conditioning on the event that the observed data are generated by the model, one conditions on the event that the model generates data close to the observed data, in a distributional sense. When closeness is defined in terms of relative entropy, the resulting "coarsened" posterior can be approximated by simply tempering the likelihood-that is, by raising the likelihood to a fractional power-thus, inference can usually be implemented via standard algorithms, and one can even obtain analytical solutions when using conjugate priors. Some theoretical properties are derived, and we illustrate the approach with real and simulated data using mixture models and autoregressive models of unknown order.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.