Bayesian Inference

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Overview

Bayesian inference combines prior beliefs with observed data to form a posterior distribution over unknown quantities, supporting predictions and decisions that account for uncertainty. Common approaches include Markov chain Monte Carlo, variational inference, Laplace approximations, and simulation-based inference; neural networks and normalizing flows increasingly help approximate or amortize these computations. Applications range from scientific inverse problems and time-series models to causal analysis, machine learning, and robotics. A central challenge is making inference scalable without losing posterior accuracy or calibrated uncertainty, especially when priors or likelihood models are misspecified.

Latest papers

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318 papers48 papers in the last four weeks, up 109% on the four weeks before. 0.3% of all new papers.

  1. Possibility-based Inference: A Deterministic Framework for Robust and Reliable Uncertainty Quantification in Decision-Making

    Oct 29, 2026Seyedfakhredin Musavishavazi, Maryam AlipourUncertainty QuantificationDecision-Making under Uncertainty

    Federal Institute for Occupational Safety and Health, Fabricestraße 8, 01099 Dresden, Germany

  2. Rènyi-Regularized Flow-Based Variational Inference for Bayesian Calibration

    Oct 27, 2026Exaucè Luweh Adjim Ngarti, Élise Arnaud, Léo Nicoletti +2Variational InferenceBayesian Inference

    AIRSEA · Mines Saint-Étienne MSE, LIMOS, FAYOL-ENSMSE, FAYOL-ENSMSE

  3. Amortized Localized Posterior Inference for Model Calibration with Application to an Ocean Turbulence Closure

    Oct 27, 2026Exaucé Luweh Adjim Ngarti, Élise Arnaud, Léo Nicoletti +2Model CalibrationBayesian Inference

    AIRSEA · Mines Saint-Étienne MSE, LIMOS, FAYOL-ENSMSE, FAYOL-ENSMSE

  4. When Respondents Are Scarce: LLM-Derived Semantic Priors for Bayesian Confirmatory Factor Analysis

    Oct 26, 2026Shaohuai Feng, Mohd Wira Mohd Shafiei, Mohd Sukri Shafie +1Latent Variable ModelsPsychometric Validation

    Centre for Global Sustainability Studies (CGSS), Universiti Sains Malaysia, Minden, Penang, Malaysia. · Centre for Education and Training in Renewable Energy, Energy Efficiency and Green Technology (CETREE), Universiti Sains Malaysia, Minden, Penang, Malaysia. · Centre for Instructional Technology and Multimedia, Universiti Sains Malaysia, Minden, Penang, Malaysia.

  5. Dimension-free regimes for unadjusted Langevin in ridge-separable models: A subspace robust Wasserstein analysis

    Oct 22, 2026Yuansi Chen, Francesco Pedrotti, Peter A. WhalleyLangevin DynamicsHigh-Dimensional Asymptotics

    Seminar for Statistics, ETH Zürich · Department of Statistics, University of Warwick

  6. The Bayesian Learning Rule Beyond KL Geometry

    Oct 22, 2026Sophia Sklaviadis, Wu Lin, Andre Martins +3Bayesian InferenceVariational Inference

    Instituto de Telecomunicações and Instituto Superior Técnico, Universidade de Lisboa, Portugal · University of Central Florida, USA · RIKEN Center for Advanced Intelligence Project, Japan +1

  7. Exact Spectral Uncertainty Decomposition for Linear-Gaussian Bayesian M/EEG Inverse Problems

    Oct 21, 2026Ismail Huseynov, Stefan HaufeUncertainty QuantificationUncertainty Decomposition

    Physikalisch-Technische Bundesanstalt, Berlin, Germany · Technische Universitat Berlin, Berlin, Germany · Charité–Universitätsmedizin Berlin, Berlin, Germany

  8. 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

  9. Alternative Strategies for Active Learning with Bayesian Bradley Terry Model

    Oct 21, 2026Saman Jayasinghe, Sean Walton, Alma Rahat +2Pairwise RankingActive Preference Learning

    Swansea University, United Kingdom · Loughborough University, United Kingdom · Bath Spa University, United Kingdom +1

  10. From Prompts to Posteriors: Can LLM Agents Perform Bayesian Inference in Context?

    Oct 21, 2026Linus Filbry, Luckeciano C. Melo, Yarin GalBelief Updating in LLMsBayesian Inference

    OATML, University of Oxford

  11. Learning from Automatic 3D Gaussian Splatting Edits for Online Bayesian Scene Change Detection

    Oct 20, 2026Changrui Zhu, Simon Julier3D Scene Change DetectionChange-Point Detection

    University College London

  12. Trail-Weighted Influence Scores for Approximate Outcome Prediction in Bayesian Networks

    Oct 19, 2026Sahil Kumar Rajput, Randhir KumarBayesian NetworksBayesian Inference

  13. R2-GP: Variance-Calibrated Priors for Additive Gaussian Processes

    Oct 19, 2026Javier Enrique Aguilar, Soham Mukherjee, David KohnsGaussian ProcessesBayesian Inference

    TU Dortmund, RC TRUST (UA Ruhr), Germany · Aalto University, Finland

  14. Martingale Posterior Cox Processes

    Oct 17, 2026Joshua Rooijakkers, Edwin FongTemporal Point ProcessesBayesian Inference

    Imperial College London · University of Hong Kong

  15. HSPR: Recovering Software-Agent Benchmark Scores via Hierarchical Posterior Inference

    Oct 17, 2026Wanyi Chen, Wanqin Shen, Yuanchen Shi +2AI Agent EvaluationActive Learning

    Soochow University · Singapore Management University

  16. Denoising Implicit Variational Inference

    Oct 17, 2026Zitong Wang, Zekun Wu, Longlin Yu +1Variational InferenceBayesian Inference

    School of Mathematical Sciences, Peking University

  17. ENFR: Evidence Networks with Flow-Matched References for Amortized Simulation-Based Model Inference

    Oct 16, 2026Kai Lehman, Jameson Dong, Carolina Cuesta-Lazaro +5Bayesian InferenceSimulation-Based Inference

    Universitäts-Sternwarte München, Fakultät für Physik, Ludwig-Maximilians-Universität, Scheinerstr. 1, 81679 München, Germany · Center for Computational Astrophysics, Flatiron Institute, 162 5th Avenue, New York, NY, 10010, USA · Excellence Cluster ORIGINS, Boltzmannstr. 2, 85748 Garching, Germany +8

  18. Pathwise Shrinkage and Probabilistic Calibration of the Multiplicative Half-Cauchy Process

    Oct 15, 2026Siddhesh KulkarniBayesian InferenceHeavy-Tailed Distributions

    a Bristol Myers Squibb, 5 Giralda Farms, Madison, NJ 07940, USA

  19. Time-partitioning Gaussian Process Mixture Model

    Oct 14, 2026Guilherme Piantino, Hedibert F. LopesTime Series ModelingNon-Stationary Time Series

    Insper Institute of Education and Research, São Paulo

  20. Joint Neural Posterior Estimation and Model Selection for Chemical Reaction Networks

    Oct 13, 2026J. Arturo Esquivel, Jorge A. Campos-Gonzalez-Angulo, Sergio Pablo-García +2Bayesian InferenceNeural Posterior Estimation

    Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada · Schools of Mathematics & Physics and Chemistry & Molecular Biosciences, The University of Queensland, St Lucia, Queensland, Australia · Australian Research Council Centre of Excellence in Quantum Biotechnology, St Lucia, Queensland, Australia +9

  21. Bayesian Test-Time Inference of Task-Aligned Similarity from Weak Interactive Feedback

    Oct 13, 2026Weida Liang, Kenji KawaguchiInference-Time Language Model AlignmentBayesian Inference

    National University of Singapore

  22. Semi-Amortized Bayesian Inference with Reusable Local Posteriors

    Oct 13, 2026Niek Stevenson, Paul-Christian Bürkner, Andrew Heathcote +1Bayesian InferenceAmortized Inference

    University of Amsterdam · TU Dortmund University · Rensselaer Polytechnic Institute

  23. 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

  24. Variational Filtering for Bayesian Last Layers with an Independent Binary Probit Surrogate

    Oct 12, 2026Dimitrije Markovic, Conor Heins, Alexander Tschantz +1Bayesian Neural NetworksBayesian Inference

    Independent Researcher Vienna, Austria · Independent Researcher Berlin, Germany · University of Sussex Brighton, UK +1

  25. NFDiag\texttt{NFDiag}: High-dimensional MCMC Convergence Diagnosis with Normalizing Flows

    Oct 12, 2026Kutay Nazlı, Elena SellentinMarkov Chain Monte CarloBayesian Inference

    Leiden Observatory, Leiden University, Einsteinweg 55 2333 CC Leiden, Netherlands · Mathematical Institute, Leiden University, Einsteinweg 55 2333 CC Leiden, Netherlands

  26. Characterizing the Separability Gap in Prior-Data Fitted Networks

    Oct 10, 2026Julius Kobialka, Manuel Hülskamp, Thomas Nagler +1Prior-Data Fitted NetworksBayesian Inference

    Department of Statistics, LMU Munich · Munich Center for Machine Learning

  27. Contrastive Linearized Laplace: Loss Geometry, Valid Repairs, and Reliable Cores

    Oct 10, 2026Francisco J. Sáez-Maldonado, Luis A. Ortega, Andrés R. Masegosa +1Contrastive LearningUncertainty Calibration

    University of Granada · Aalborg University

  28. How Well Do Prior-Data Fitted Networks Amortize Bayesian Inference?

    Oct 9, 2026Manuel Hülskamp, Julius Kobialka, Emanuel Sommer +2Bayesian Model AveragingPrior-Data Fitted Networks

    LMU Munich and Munich Center for Machine Learning

  29. Model-free Bayesian Clustering and Multimodal Posterior Summarisation

    Oct 9, 2026Qing Li, Sara WadeClusteringUnsupervised Clustering

    School of Mathematics University of Edinburgh

  30. Product Monte Carlo: Expectation Under a Product of Densities

    Oct 8, 2026Pietro Carlotti, Sifan Liu, Yuling YaoBayesian Inference

    University of Texas at Austin, Department of Statistics and Data Sciences · Duke University, Department of Statistical Science