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. Bayesian Hierarchical Models and the Maximum Entropy Principle

    Mar 10, 2026Brendon J. BrewerHierarchical Bayesian ModelingBayesian Inference

    Department of Statistics, The University of Auckland

  2. Beyond Heuristic Prompting: A Concept-Guided Bayesian Framework for Zero-Shot Image Recognition

    Mar 9, 2026Hui Liu, Kecheng Chen, Jialiang Wang +3Zero-Shot Image ClassificationZero-Shot Learning

    City University of Hong Kong · Harbin Institute of Technology · Nanyang Technological University

  3. A Distributed Gaussian Process Model for Multi-Robot Mapping

    Mar 7, 2026Seth Nabarro, Mark van der Wilk, Andrew J. DavisonSparse Gaussian ProcessesGaussian Belief Propagation

    Dyson Robotics Lab, Imperial College London · Department of Computer Science, University of Oxford

  4. Scalable Posterior Uncertainty for Flexible Density-Based Clustering

    Mar 3, 2026Nicola Bariletto, Stephen G. WalkerDensity-Based ClusteringClustering

    Department of Statistics and Data Sciences, UT Austin

  5. Generalized Bayes for Causal Inference

    Mar 3, 2026Emil Javurek, Dennis Frauen, Yuxin Wang +1Generalized Bayesian InferenceCausal Inference

    LMU Munich · Munich Center for Machine Learning

  6. Probabilistic Learning and Generation in Deep Sequence Models

    Mar 1, 2026Wenlong ChenBayesian InferenceSequence Modeling

    Imperial College London Department of Computing

  7. General Bayesian Policy Learning

    Feb 27, 2026Masahiro KatoGeneralized Bayesian InferencePolicy Learning

    1Mizuho-DL Financial Technology Co., Ltd., Tokyo, Japan

  8. Beyond NNGP: Large Deviations and Feature Learning in Bayesian Neural Networks

    Feb 26, 2026Katerina Papagiannouli, Dario Trevisan, Giuseppe Pio ZittoLarge Deviation PrinciplesBayesian Neural Networks

    Dipartimento di Matematica, Università di Pisa

  9. Calibrated Test-Time Guidance for Bayesian Inference

    Feb 25, 2026Daniel Geyfman, Felix Draxler, Jan Groeneveld +3Bayesian Inverse ProblemsBayesian Inference

    Department of Computer Science, University of California, Irvine, CA, USA · Seoul National University, Seoul, South Korea · Chan-Zuckerberg Initiative, Redwood City, CA, USA

  10. Function-Space Empirical Bayes Regularisation with Student's t Priors

    Feb 25, 2026Pengcheng Hao, Ercan Engin KuruogluBayesian Neural NetworksBayesian Inference

    Institute of Data and Information, Tsinghua Shenzhen International Graduate School, Shenzhen, China

  11. Sample-efficient evidence estimation of score based priors for model selection

    Feb 24, 2026Frederic Wang, Katherine L. BoumanBayesian InferenceDiffusion-Based Inverse Problems

    Department of Computing and Mathematical Sciences, Caltech, Pasadena, CA 91125, USA

  12. On the Equivalence of Random Network Distillation, Deep Ensembles, and Bayesian Inference

    Feb 23, 2026Moritz A. Zanger, Yijun Wu, Pascal R. Van der Vaart +2Uncertainty QuantificationBayesian Inference

    Delft University of Technology, Delft, 2628 XE, The Netherlands

  13. Prognostics of Multisensor Systems with Unknown and Unlabeled Failure Modes via Bayesian Nonparametric Process Mixtures

    Feb 22, 2026Kani Fu, Sanduni S Disanayaka Mudiyanselage, Chunli Dai +1Dirichlet Process Mixture ModelsPredictive Maintenance

    Department of Industrial and Systems Engineering, University of Florida, Gainesville, USA · School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, USA

  14. Distributional Stability of Tangent-Linearized Gaussian Inference on Smooth Manifolds

    Feb 22, 2026Junghoon Seo, Hakjin Lee, Jaehoon SimBayesian Inference

    AI Robot Team, PIT IN Corp., South Korea.

  15. Robust Predictive Uncertainty and Double Descent in Contaminated Bayesian Random Features

    Feb 22, 2026Michele Caprio, Katerina Papagiannouli, Siu Lun Chau +1Random Feature MethodsBayesian Inference

    The University of Manchester, UK · University of Pisa, Italy · Nanyang Technological University, Singapore +1

  16. Equivalence and Divergence of Bayesian Log-Odds and Dempster's Combination Rule for 2D Occupancy Grids

    Feb 21, 2026Tatiana Berlenko, Kirill KrinkinOccupancy Grid MappingDempster-Shafer Theory

    Constructor University, Bremen, Germany · JetBrains LTD

  17. Vectorized Bayesian Inference for Latent Dirichlet-Tree Allocation

    Feb 21, 2026Zheng Wang, Nizar BouguilaTopic ModelingBayesian Inference

    Concordia Institute for Information Systems Engineering Concordia University Montreal, QC H3G 1M8, Canada

  18. Calibrated Adaptation: Bayesian Stiefel Manifold Priors for Reliable Parameter-Efficient Fine-Tuning

    Feb 19, 2026Ibne Farabi Shihab, Sanjeda Akter, Anuj SharmaBayesian LoRAParameter-Efficient Fine-Tuning

    Department of Computer Science, Iowa State University · Department of Civil, Construction and Environmental Engineering, Iowa State University

  19. MDP Planning as Policy Inference

    Feb 19, 2026David TolpinVariational Monte CarloMarkov Decision Processes

    Offtopia

  20. Bayesian Optimality of In-Context Learning with Selective State Spaces

    Feb 19, 2026Di Zhang, Jiaqi XingIn-Context LearningSelective SSMs

    School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou, Jiangsu, China

  21. B3^3-Seg: Camera-Free, Training-Free 3DGS Segmentation via Analytic EIG and Beta-Bernoulli Bayesian Updates

    Feb 19, 2026Hiromichi Kamata, Samuel Arthur Munro, Fuminori Homma3DGS SegmentationOpen-Vocabulary 3D Segmentation

    Sony Group Corporation, Tokyo, Japan · Pixomondo, London, United Kingdom

  22. Universal priors: solving empirical Bayes via Bayesian inference and pretraining

    Feb 16, 2026Nick Cannella, Anzo Teh, Yanjun Han +1Synthetic Data PretrainingEmpirical 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

  23. Neural Architectures for Amortized Bayesian Inference: Statistical Foundations and Empirical Assessments

    Jan 12, 2026Roy Shivam Ram Shreshtth, Arnab Hazra, Gourab MukherjeeUncertainty QuantificationAmortized Inference

    Department of Statistics and Data Science, Indian Institute of Technology Kanpur, Kanpur, India 208016 · Marshall School of Business, University of Southern California, Los Angeles, United States 90089-0809

  24. JADAI: Jointly Amortizing Adaptive Design and Bayesian Inference

    Dec 28, 2025Niels Bracher, Lars Kühmichel, Desi R. Ivanova +3Parameter EstimationAmortized Inference

    1Rensselaer Polytechnic Institute, USA · 2TU Dortmund University, Germany · University of Oxford, UK.

  25. Ideal Observer for Segmentation of Dead Leaves Images

    Dec 5, 2025Swantje Mahncke, Malte Ott, Lars C. Reining +1Image SegmentationOcclusion Reasoning

    Centre for Cognitive Science, Institute of Psychology, TU Darmstadt, Germany · Center for Mind, Brain and Behavior (CMBB), Universities of Marburg, Giessen, and Darmstadt, Germany · Department of Mathematics, TU Darmstadt, Germany

  26. Flexible Gravitational-Wave Parameter Estimation with Transformers

    Dec 2, 2025Annalena Kofler, Maximilian Dax, Stephen R. Green +6Transformer InferenceGravitational-Wave Astronomy

    Max Planck Institute for Intelligent Systems, Max-Planck-Ring 4, 72076 Tübingen, Germany · ELLIS Institute Tübingen, Maria-von-Linden-Straße 2, 72076 Tübingen, Germany · Tübingen AI Center, Maria-von-Linden-Straße 1, 72076 Tübingen, Germany +4

  27. Graded strength of comparative illusions is explained by Bayesian inference

    Nov 18, 2025Yuhan Zhang, Erxiao Wang, Cory ShainBayesian InferenceComputational Psycholinguistics

    Margaret Jacks1DepartmentHall, Buildingof Linguistics,460 Rm.Stanford127, Stanford,UniversityCA ,94305-2150

  28. Robust Experimental Design via Generalised Bayesian Inference

    Nov 10, 2025Yasir Zubayr Barlas, Sabina J. Sloman, Samuel KaskiGeneralized Bayesian InferenceBayesian Experimental Design

    The University of Manchester · Aalto University

  29. Estimating Orbital Parameters of Direct Imaging Exoplanet Using Neural Network

    Oct 20, 2025Bo Liang, Hanlin Song, Chang Liu +9Markov Chain Monte CarloSimulation-Based Inference

    Center for Gravitational Wave Experiment, National Microgravity Laboratory, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China · Taiji Laboratory for Gravitational Wave Universe (Beijing/Hangzhou), University of Chinese Academy of Sciences (UCAS), Beijing 100049, China · National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China +6

  30. Neural Variational Cut Posteriors without Upstream Data

    Oct 11, 2025Jiafang Song, Sandipan Pramanik, Abhirup DattaBayesian InferenceVariational Inference

    Department of Biostatistics, Johns Hopkins University, USA