Posterior Contraction

Latest papers

8 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Sample-Size Extrapolation in Simulation-Based Inference via Statistical Scaling
Oct 12, 2026Shiyi Sun, Lei Fang, Geoff K NichollsPosterior Contraction Simulation-Based Inference
Department of Statistics, University of Oxford, Oxford, United Kingdom. · Mathematical Institute, University of Oxford, Oxford, United Kingdom.
RoBART: Bayesian Additive Regression Trees with Tree-Specific Rotations
Oct 7, 2026Jeongung Heo, Seonghyun JeongPosterior Contraction Nonparametric Regression
Yonsei University
Dirichlet Process Mixtures of Trees with Gaussian Process Splits: A Bayesian Nonparametric Framework with Posterior Contraction Rate
Aug 22, 2026Subhasish Basak, Anik Roy, Sourabh BhattacharyaDirichlet Process Mixture Models Posterior Contraction
Indian Statistical Institute
Posterior Contraction of Lévy Adaptive B-spline Regression in Besov Spaces
May 19, 2026Jeunghun Oh, Sewon Park, Jaeyong LeeBayesian Inference Posterior Contraction
Department of Statistics, Seoul National University. · Department of Statistics, Sookmyung Women’s University.
Posterior Contraction Rates for Sparse Kolmogorov-Arnold Networks in Anisotropic Besov Spaces
May 12, 2026Jeunghun Oh, Kyeongwon Lee, Jaeyong Lee +1Bayesian Neural Networks Posterior Contraction
Department of Statistics, Seoul National University · Department of Statistics, Inha University · Department of Mathematics, University of Maryland
On Bayesian Softmax-Gated Mixture-of-Experts Models
Apr 22, 2026Nicola Bariletto, Huy Nguyen, Nhat Ho +1Bayesian Mixture Models Bayesian Inference
The University of Texas at Austin
SPDE Methods for Nonparametric Bayesian Posterior Contraction and Laplace Approximation
Mar 23, 2026Enric Alberola-Boloix, Ioar Casado-TelletxeaPosterior Contraction Stochastic PDEs
Basque Center for Applied Mathematics (BCAM), Bilbao (Spain)
Universal priors: solving empirical Bayes via Bayesian inference and pretraining
Feb 16, 2026Nick Cannella, Anzo Teh, Yanjun Han +1Synthetic Data Pretraining Empirical Bayes
Courant Institute of Mathematical Sciences, NYU, New York City, NY · Department of EECS, MIT, Cambridge, MA · Courant Institute of Mathematical Sciences and Center for Data Science, NYU, New York City, NY

Oct 12, 2026J/K move · Enter open · S save

Sample-Size Extrapolation in Simulation-Based Inference via Statistical Scaling

Shiyi Sun, Lei Fang, Geoff K Nicholls

Department of Statistics, University of Oxford, Oxford, United Kingdom. · Mathematical Institute, University of Oxford, Oxford, United Kingdom.

Posterior Contraction Simulation-Based Inference Neural Posterior Estimation Amortized Inference

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In simulation-based inference (SBI), neural posterior estimators trained on a bounded range of sample sizes can become inaccurate on larger datasets because their parameterizations do not specify how posterior location, uncertainty and shape must change as observations accumulate. We propose center-estimated NPE (CE-NPE), which combines a statistical center with an explicit contraction rate and learns a local center correction and residual densities in posterior-scale coordinates. The center is a statistical estimate, such as a closed-form statistic or a simulator-based moment match, which does not require an evaluable likelihood. Our analysis explains how contraction amplifies center error and decomposes Gaussian posterior error into shape, mean and covariance contributions. On six regular benchmarks, CE-NPE gives the best reference-posterior agreement of all tested amortized estimators at every out-of-range size, up to five times the largest training size, without retraining; it also fits better within the training range on most of them. Matched controls and nonregular examples characterize the scope of these gains and identify the conditions on which they depend: an appropriate contraction rate, an accurate center, and a stable residual representation.