Posterior Sampling

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Overview

Posterior sampling studies how to generate representative draws from a probability distribution over unknown parameters or explanations, so uncertainty can inform predictions and decisions. Classical approaches include Markov chain Monte Carlo methods such as Hamiltonian and Langevin dynamics, as well as sequential Monte Carlo; newer methods use diffusion and flow-based generative models to accelerate sampling, especially for inverse problems with learned priors. In reinforcement learning and bandits, Thompson sampling uses posterior draws to balance exploration and reward, while other applications include scientific data assimilation, imaging, and simulation-based inference. A central challenge is making sampling fast and scalable without sacrificing calibration, coverage of multiple modes, or guarantees that the draws match the target posterior.

Latest papers

CardsList

69 papers14 papers in the last four weeks, down 7% on the four weeks before. 0.1% of all new papers.

  1. Generalizing Metropolis-Hastings for DAG Sampling

    Oct 21, 2026Daniele Nikzad, Mikko KoivistoBayesian Network Structure LearningMarkov Chain Monte Carlo

    Department of Computer Science, University of Helsinki, Helsinki, FI-00014, Finland

  2. Asynchronous, Generation-Free ABC: Streaming AMIS Reweighting for Heterogeneous Simulator Workloads on HPC

    Oct 21, 2026Julian Herold, Markus Götz, Alexander SchugSimulation-Based InferencePosterior Sampling

    Scientific Computing Center, Karlsruhe Institute of Technology, Karlsruhe, Germany

  3. Test-Time Scaling of Text-to-Image Flow Matching Models with Noise-Rejuvenated Sequential Monte Carlo

    Oct 18, 2026Wenyi Lian, Zheng Zhao, Thomas B. SchönInference-Time ScalingT2I Generation

    Department of Information Technology Uppsala University, Sweden · Department of Computer and Information Science Linköping University, Sweden

  4. The Tilted Sampling Problem: Optimism, Posterior Sampling, and Logarithmic Regret

    Oct 15, 2026Mohammad Pedramfar, Siamak RavanbakhshPosterior SamplingThompson Sampling

    Mila – Québec AI Institute, McGill University

  5. On Inherent Privacy of Posterior Sampling: A Unified Rényi-Divergence Framework

    Oct 14, 2026Ruyu Zhou, Fang LiuRényi Differential PrivacyPosterior Sampling

    Department of Computer Science and Statistics University of Rhode Island · Department of ACMS University of Notre Dame

  6. Latent Annealed Posterior Sampling with a Variance-Preserving Bridge for Sparsely Observed Random Fields

    Oct 14, 2026Jiankun Lei, Tian QiPosterior SamplingBayesian Inverse Problems

    School of Statistics and Mathematics, Yunnan University of Finance and Economics, No. 237 Longquan Road, Wuhua District, Kunming, 650221, Yunnan, China

  7. BridgeTwist: Twisting Schrödinger Bridges for Training-Free Conditional Sampling

    Oct 13, 2026Mengyu Li, Qianqian Qu, Jun S. LiuConditional Generative ModelingPosterior Sampling

    Department of Statistics and Data Science, Tsinghua University, Beijing, China

  8. SCOPE Before You Sample: Conformal Scores Guide Posterior Exploration for Fast and Calibrated Bayesian Predictive Inference

    Oct 12, 2026Zahra Moslemi, Ziyi Liang, Babak ShahbabaBayesian InferencePosterior Sampling

    University of California, Irvine · University of California, Santa Barbara

  9. Sampling from Neural Operator Priors to Solve Partial Differential Equations

    Oct 12, 2026Andrew Millard, Joshua Murphy, Zheng ZhaoPDE SolvingPartial Differential Equations

    Department of Computer and Information Science, Linköping University, Sweden. · Department of Political Science, Trinity College Dublin, Ireland.

  10. Spanning Diffusion and Flow-based Posterior Sampling via Exact Gaussian Conditioning

    Oct 9, 2026Teo Deveney, Lea Bogensperger, Hok Shing WongPosterior SamplingDiffusion Posterior Sampling

    University of Bath · University of Zurich · LMU Munich

  11. Amortised Inference through One-Step Implicit Sampling

    Oct 8, 2026Vincent Pauline, Kirill Tamogashev, Arran Carter +3Amortized InferencePosterior Sampling

    Technical University of Munich · Munich Center for Machine Learning · University of Edinburgh +1

  12. Recovery Guarantees for Posterior Sampling of One-Bit Compressed Sensing

    Oct 8, 2026Jing Ma, Yujia Wu, Zhaoqiang LiuCompressed SensingSample Complexity

    University of Electronic Science and Technology of China

  13. Twist Flow for Inverse Problems

    Oct 7, 2026Shiqin Zeng, Zijun Deng, Felix J. HerrmannBayesian Inverse ProblemsPosterior Sampling

    Georgia Institute of Technology

  14. Direct Intermediate Initialization for Tilted Diffusion Samplers

    Oct 5, 2026Gregory D. BellchambersPosterior SamplingDiffusion-Based Inverse Problems

    PhysicsX Ltd. London, EC2A 4DQ

  15. A Unified Framework for Bayesian Data Assimilation with Generative Models and Observation Interpolants

    Oct 2, 2026Nikolaj T. Mücke, Benjamin SanderseData AssimilationPosterior Sampling

    Scientific Computing, Centrum Wiskunde & Informatica · Faculty of Civil Engineering and Geosciences, Delft University of Technology · Centre for Analysis, Scientific Computing and Applications, Eindhoven University of Technology

  16. Flow Matching for Fast Posterior Sampling in Bayesian Inverse Problems

    Oct 1, 2026Jan Blechschmidt, Oliver G. Ernst, Moritz Poguntke +1Posterior SamplingBayesian Inverse Problems

  17. Posterior sampling by source-space MCMC via prior-based few-step transport maps

    Oct 1, 2026Hoang Phuc Hau Luu, Marcelo Hartmann, Zhongjian WangMarkov Chain Monte CarloBayesian Inference

    Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore. · Department of Computer Science, Faculty of Science, University of Helsinki, Finland.

  18. Specificity-Aware Diffusion Steering via Variance-Reduced Sequential Monte Carlo

    Sep 30, 2026Luran Wang, Linrui Ma, Hannes Stärk +1Diffusion Model GuidanceDiffusion Guidance

    CSAIL, Massachusetts Institute of Technology

  19. SNaP: One-Step Posterior Sampling for Noisy Inverse Problems

    Sep 28, 2026Shirin Shoushtari, Edward P. Chandler, Xiao Shi +1Flow MatchingMeanFlow

    WashU · UW-Madison

  20. Minimax-Optimality of Posterior Sampling for Reinforcement Learning

    Sep 27, 2026Taewon Goo, Kihyuk HongRegret Minimization in RLMarkov Decision Processes

    KAIST

  21. Nuclear Norm-Regularized Bayesian Matrix Completion

    Sep 24, 2026Calvin TolbertNuclear Norm MinimizationPosterior Sampling

    School of Operations Research and Information Engineering, Cornell University

  22. Local Geometric Mixing via Dobrushin Contraction with Applications to Diffusion Path Monte Carlo and the Proximal Sampler

    Sep 23, 2026Stefan OberdörsterMarkov Chain Monte CarloPosterior Sampling

    Institute for Applied Mathematics, University of Bonn

  23. Auditing Bayesian Graph Alignment: Diagnostic Comparisons and Reference Failure

    Sep 19, 2026Melika Gorgi, Kourosh MirsohiMarkov Chain Monte CarloBenchmark Auditing

    Center for Complex Biological Systems University of California, Irvine Irvine, CA 92697, USA · Department of Computer Science University of California, Irvine Irvine, CA 92697, USA

  24. How Many Posterior Samples? Calibrated Stopping for Adaptive Sensing

    Sep 18, 2026Vincent Corlay, Andriy EnttselActive PerceptionOptimal Stopping

    Mitsubishi Electric R&D Centre Europe

  25. Aggregated Posterior Predictive Checks for Generative Modeling

    Sep 17, 2026Shweta Dutta, Gemma E. MoranGenerative ModelingLatent Variable Models

    Department of Statistics, Rutgers University

  26. FlowSGS: Improving Flow Priors for Inverse Imaging with Stochastic Interpolants

    Sep 17, 2026Tianao Li, Xinhui Qian, Emma AlexanderFlow MatchingMarkov Chain Monte Carlo

    Department of Computer Science, Northwestern University · NSF-Simons AI Institute for the Sky (SkAI) · Department of Statistics and Data Science, Northwestern University

  27. Quenched Ensemble Sampling

    Sep 14, 2026David YallupBayesian InferencePosterior Sampling

    Kavli Institute for Cosmology Cambridge Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge, CB3 0HA, UK

  28. GRPO-QPS: Target-Preserving Reinforcement Learning for Quantum Posterior Sampling

    Sep 13, 2026Yufeng Wang, Parivesh Priye, Lu Wei +1Independent Metropolis-HastingsMarkov Chain Monte Carlo

    Stony Brook University · Georgia Institute of Technology · Westlake University

  29. High-Magnetization Sampling at Low Temperatures: Ising Models and Bayesian Sparse Linear Regression

    Sep 8, 2026Syamantak Kumar, Purnamrita Sarkar, Kevin Tian +1Sparse RecoveryIsing Model

    University of Texas at Austin

  30. Learning Informative Prior with Infinite-Dimensional Continuous Normalizing Flow for Bayesian Inverse Problem

    Sep 3, 2026Yang Zhao, Junxiong Jia, Tao ZhouNormalizing FlowsBayesian Inverse Problems

    School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, China · Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing, 100190, China