Bayesian Inference

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20 papers in the last four weeks, up 300% on the four weeks before. 0.2% of all new papers.

Jul 13Week of Sep 28

Latest papers 219

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From Drift to Coherence: Stabilizing Beliefs in LLMs
Jun 16, 2026SongEun Kim, Seungyoo Lee, Edwin Fong +2LLM Fine-Tuning Bayesian Inference
Department of Statistics, Seoul National University · Korea Advanced Institute of Science & Technology · The University of Hong Kong +1
A Bayesian Boolean Matrix Factorization with Application to Copy Number Analysis in Cancer
Jun 16, 2026Adolphus Wagala, Mehmet Samur, Giovanni ParmigianiCancer Genomics Bayesian Inference
Department of Data Science, Dana-Farber Cancer Institute · Department of Biostatistics, Harvard T.H. Chan School of Public Health
Credibility-Weighted Pricing of Autonomous Vehicle Liability Under Operational Design Domain Shift
Jun 16, 2026Doyeon JangBayesian Inference Autonomous Driving
Doyeon Jang
Bayesian Inference and Decision Audits for Public Archives of Frontier AI Evaluations
Jun 15, 2026Yanan LongLLM Evaluation Benchmark Auditing
StickFlux Labs
Dynestyx: A Probabilistic Programming Library for Dynamical Systems
Jun 15, 2026Daniel Waxman, Dmitry Batenkov, John Feser +4Dynamical Systems Parameter Estimation
1Basis Research Institute · 2Massachusetts Institute of Technology
Stop the Sampler! Classifier-Based Adaptive Stopping for Sampling Kernels
Jun 15, 2026Kirill Korolev, Nikita Morozov, Stepan Pavlenko +2Markov Chain Monte Carlo Bayesian Inference
1HSE University · University of Edinburgh · 3CIFAR Fellow.
Nonlocal Bayesian Modeling of Continuous Spatio-Temporal Dynamics
Jun 12, 2026Jaeyeong Lee, Heeyoung KimIrregular Time-Series Modeling Bayesian Inference
Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
Implicit Variational Rejection Sampling
Jun 12, 2026Jian Xu, Shigui Li, Wei Chen +6Bayesian Inference Variational Inference
RIKEN iTHEMS · RIKEN AIP · South China University of Technology +2
Is It You or Your Environment? A Bayesian Inference Framework for Genomically-Anchored Personalized Physiological Interpretation
Jun 11, 2026Aruna Dey, Suraj BiswasHealthcare Bayesian Inference
Dots-In
Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions
Jun 10, 2026Owen O'Neill, Fintan CostelloSelective Prediction Bayesian Inference
University College Dublin, Ireland
Conformal Bayes under Label Shift: Post-Hoc Calibration vs. In-Training Adaptation
Jun 10, 2026Seungjin ChoiWeighted Conformal Prediction Label Shift
1CROID Research and aSSIST University, Seoul, Korea.
Population-Aware Physics-Informed Neural Particle Flow for Bayesian Update
Jun 9, 2026Batu Candan, Simone ServadioBayesian Inference Deep Sets
Iowa State University, Ames, IA 50011, USA
KG-SoftMAP: Soft Knowledge-Graph Priors for Bayesian Network Structure Learning from Sparse Discrete Data
Jun 9, 2026Guoliang Xu, James E. CorterGraph Structure Learning Bayesian Networks
Columbia University
Nonlinear Bayesian Estimator for Parameter Learning: A Fixed-Point Characterization
Jun 8, 2026Sasan Vakili, Daniël Woonings, Pradyumna Paruchuri +1Parameter Estimation Bayesian Inference
Delft University of Technology, The Netherlands · University of Toronto, Canada
Bayesian Selective Latent Inference for Wastewater-First Influenza Monitoring
Jun 8, 2026Yixuan Zhang, Yang Song, Hao Wang +2Epidemic Forecasting Selective Prediction
Section of Health Data Science and AI, Department of Public Health, University of Copenhagen, Copenhagen, Denmark · Section of Health Data Science & AI, Department of Public Health, University of Copenhagen, Copenhagen, Denmark · Rutgers University, New Brunswick, NJ, USA +1
Performance Evaluation of Social Learning
Jun 8, 2026Felice Scala, Marco Carpentiero, Vincenzo Matta +1Bayesian Inference Decentralized Learning
DIEM, University of Salerno, I-84084 Fisciano (SA), Italy · CNIT, Italy · EPFL, CH-1015 Lausanne, Switzerland
Predictive Coding with Bayesian Priors via Proximal Gradients
Jun 6, 2026Francesco BulloPredictive Coding Markov Random Fields
Mechanical Engineering, 2325 Engineering Bldg II, UCSB, Santa Barbara, CA 93106-5070, USA
Large-scale empirical tuning and comparison of default optimizers for variational inference
Jun 5, 2026Trevor Campbell, Jonathan H. Huggins, Kyurae Kim +1Stochastic Optimization Bayesian Inference
Department of Statistics, UBC · Department of Mathematics & Statistics, Boston University · Faculty of Computing & Statistics, Boston University +1
Structure-Preserving Correction Learning for Sparse Bayesian Inference in Brain Source Imaging
Jun 5, 2026Marco Morik, Xiao Ruiting, Shinichi Nakajima +2Electroencephalography Bayesian Inverse Problems
Berlin Institute for the Foundations of Learning and Data (BIFOLD), Germany · Technische Universität Berlin, Germany · RIKEN Center for Advanced Intelligence Project (AIP), Japan +2
Disentangling Latent Risk Pathways via Bayesian Hypergraph Inference
Jun 4, 2026Shengxian Ding, Haonan Gao, Pangpang Liu +2Latent Variable Models Bayesian Inference
Department of Biostatistics, Yale University, New Haven, USA
Equivariant Neural Belief Propagation
Jun 4, 2026Zehua Cheng, Wei Dai, Jiahao SunFactor Graph Optimization Gaussian Mixture Models
Department of Computer Science, University of Oxford, Oxford, United Kingdom · FLock.io, London, United Kingdom
Breaking Time: A Fully Gaussian Framework for Distributed and Continuous-Time SLAM
Jun 4, 2026Davide Ceriola, Simone Ferrari, Luca Di Giammarino +2Simultaneous Localization and Mapping Bayesian Inference
Department of Computer, Control, and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, Italy · University of Stuttgart, Germany
Environment-Robust Representation Learning with Empirical Bayes
Jun 3, 2026Yuli Slavutsky, Matthew Shen, Bohan Wu +1Latent Variable Models Empirical Bayes
Department of Statistics Columbia University New York, NY 10027, USA · Departments of Statistics and Computer Science Columbia University New York, NY 10027, USA
Bayesian learning for the stochastic shortest path problem
Jun 3, 2026Chon Wai Ho, Sumeetpal S. Singh, Jiaqi GuoMarkov Decision Processes Reinforcement Learning
Department of Engineering, University of Cambridge, UK · School of Mathematics and Physics, University of Wollongong, Wollongong, Australia
Neural Galerkin Normalizing Flows for Bayesian Inference of Diffusions with Inaccessible Boundaries
Jun 3, 2026Riccardo Saporiti, Fabio NobileNormalizing Flows Neural PDE Solvers
CSQI École Polytechnique Fédérale de Lausanne Lausanne, Switzerland 1015
Bayesian meta-learning for modeling Alzheimer's disease progression
Jun 1, 2026Clara Hoffmann, Nadja KleinAlzheimer's Disease Meta-Learning
Scientific Computing Center, Karlsruhe Institute of Technology, Germany
Flow-Transformed Implicit Processes for Function-Space Variational Inference
Jun 1, 2026Luis A. Ortega, Andrés R. Masegosa, Thomas D. NielsenBayesian Inference Variational Inference
Aalborg University
MINTS: Minimalist Thompson Sampling
Jun 1, 2026Kaizheng WangThompson Sampling Multi-Armed Bandits
Department of IEOR and Data Science Institute, Columbia University.
Sample Complexity and Decision-Theoretic Guarantees for Bayesian Model Averaging over Decision Trees with Catalan-Exponential Priors
May 31, 2026Livija Jakaite, Vitaly SchetininSample Complexity Bayesian Inference
School of Computing and Engineering University of Bedfordshire, Luton, UK
Non-Vacuous Certification of Transport MCMC via Oscillation-Controlled Normalizing Flows
May 31, 2026Jun HuMarkov Chain Monte Carlo Normalizing Flows
China Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya 572025, China · School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China

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From Drift to Coherence: Stabilizing Beliefs in LLMs

SongEun Kim, Seungyoo Lee, Edwin Fong, Hyungi Lee+1

Department of Statistics, Seoul National University · Korea Advanced Institute of Science & Technology · The University of Hong Kong+1

LLM Fine-Tuning Bayesian Inference LLM Reliability Belief Updating in LLMs

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Large language models (LLMs) are often hypothesized to perform implicit Bayesian inference, yet a key coherence condition, the martingale property of predictive beliefs, has been shown to fail in controlled synthetic in-context learning settings. We revisit this question in a more typical usage regime: generic multiple-choice question answering. Exploiting the discrete answer space, we compute exact predictive distributions and study belief dynamics induced by autoregressive answer resampling. We introduce prompted predictive resampling (PPR), where an LLM generates a sequence of answers to the same question. Empirically, PPR reveals early-stage belief drift, indicating martingale violations. However, after sufficient resampling steps, the belief process self-stabilizes and converges to a coherent predictive distribution. Based on this observation, we further propose (i) a seed-answer prompting strategy to accelerate stabilization, and (ii) a self-consistency loss that amortizes early-stage drift into the model via fine-tuning. Experiments on multiple-choice QA benchmarks show that our methods substantially reduce belief drift and improve predictive coherence without sacrificing accuracy.

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