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.

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Latest papers 219

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Nonparametric Bayesian Inverse Reinforcement Learning with Data-Parallel Gibbs Sampling
Jul 10, 2026Sai Anirudh Katupilla, Shreeya Dasa LakshminathReinforcement Learning Bayesian Inference
University of Maryland, College Park
Neural Posterior Estimation for Inferring Weak Lensing Shear
Jul 10, 2026Tim White, Dingrui Tao, Camille Avestruz +2Simulation-Based Inference Neural Posterior Estimation
Department of Statistics, University of Michigan · Department of Physics, University of Michigan
Geometry-Aware Bayesian Quantification via Compositional Data Analysis
Jul 6, 2026Alejandro Moreo, Pablo González, Juan José del CozLabel Shift Bayesian Inference
Istituto di Scienza e Tecnologie dell’Informazione Consiglio Nazionale delle Ricerche Pisa, Italy · Artificial Intelligence Center University of Oviedo Asturias, Spain
Integrating Neural Encoders in Bayesian Generalized Linear Mixed Models for Multimodal Data
Jul 6, 2026Yuankang Zhao, Youngsoo Baek, Felipe A. Medeiros +2Bayesian Inference Hierarchical Bayesian Modeling
Department of Biostatistics & Bioinformatics, Duke University · Department of Ophthalmology, University of Miami
A Bayesian Framework for Evaluating Scenario Compatibility in Generative Population Synthesis
Jul 3, 2026Zhenlin Qin, Leizhen Wang, Yancheng Ling +1Uncertainty Quantification Bayesian Inference
Department of Civil and Architectural Engineering, KTH Royal Institute of Technology, Stockholm, Sweden. · Department of Data Science and Artificial Intelligence, Monash University, Melbourne, Australia. · Digital Futures, KTH Royal Institute of Technology, Stockholm, Sweden.
Full Bayesian Reinforcement Learning via LF-IBIS
Jul 2, 2026Stefano Masini, Cecilia Viscardi, Michela BacciniSimulation-Based Inference Reinforcement Learning
Department of Computer Science, University of Pisa, Italy · Department of Economics and Statistics, University of Salerno, Italy · Department of Statistics, Computer Science, Applications ”G.Parenti”, University of Florence, Italy
Calibration, Not Compilation: Detecting and Repairing Misspecified Probabilistic Programs Written by Language Models
Jun 30, 2026Jian Xu, Delu Zeng, John Paisley +1Automated Program Repair Bayesian Inference
RIKEN iTHEMS · RIKEN AIP · South China University of Technology +1
TDGT: A Tabular Data Generation Toolkit supporting adaptive GPU-accelerated Bayesian mixture models, diffusion-based models, and latent-space generative modeling
Jun 30, 2026Vasileios C. Pezoulas, Nikolaos S. Tachos, Eleni Georga +3Bayesian Mixture Models Bayesian Inference
SYNTHAINA AI, Ioannina, Greece
BayesBench: Evaluating LLM Belief Trajectories Under Multi-Turn Evidence Accumulation
Jun 29, 2026Ankur Samanta, Akshayaa Magesh, Tal Lancewicki +7LLM Evaluation Bayesian Inference
Meta AI · Columbia University · Work done at Meta +2
RBE-Flow: Recurrent Bayesian Estimation on Feature Manifolds for Cross-Modal Registration
Jun 29, 2026Mengzhu Ding, Xin Song, Xiaoke Ding +2Optical Flow Bayesian Inference
Northeastern University, China
Transformer Architectures as Complete Bayes Processes: A Formal Proof in the Measure-Theoretic Kernel Framework
Jun 29, 2026Haobo YangTransformer Bayesian Inference
Department of Computer Science and Engineering, SUSTech University
Normalizing Flow-Enhanced Message Passing for Multirobot Collaborative Localization
Jun 29, 2026Han Shen, Guanghui Wen, Liangming Chen +1Bayesian Inference Cooperative Localization
Department of Systems Science, Southeast University, Nanjing 211189, China · School of Automation, Southeast University, Nanjing 210096, China · School of Automation and Intelligent Manufacturing and Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology, Southern University of Science and Technology, Shenzhen 518055, China +1
Adaptive Bayes exactly tracks information over intrinsic time
Jun 26, 2026Akshay BalsubramaniBayesian Inference Online Learning
Simulation-based inference for rapid Bayesian parameter estimation in epidemiological models: a comparison with MCMC
Jun 25, 2026Alina Bazarova, Johann Fredrik Jadebeck, Henrik Zunker +5Markov Chain Monte Carlo Simulation-Based Inference
Forschungszentrum Jülich, Jülich Supercomputing Centre, Jülich, Germany · Helmholtz AI, Germany · Forschungszentrum Jülich, Institute of Bio- and Geosciences, Jülich, Germany +3
Beyond Global Divergences: A Local-Mass Perspective on Bayesian Inference
Jun 25, 2026Hanli Xu, Fengxiang He, Sarat MokaBayesian Inference
UNSW Sydney · University of Edinburgh
XMSE-Aware Adaptive Empirical Bayes Estimation
Jun 25, 2026Minghao Chen, Jiale ZhengParameter Estimation Empirical Bayes
Tencent Technology, Shenzhen, China · Huawei Noah’s Ark Lab, Shenzhen, China
Variational Inference via Entropic Transport Descent
Jun 24, 2026Vincent Pacelli, Akash Ratheesh, Evangelos TheodorouBayesian Inference Variational Inference
School of Aerospace Engineering Georgia Institute of Technology Atlanta, GA 30332
Laplace-Fisher Gate Identities for Optimal Matrix-Gated Blended Score Estimation
Jun 23, 2026Alois Duston, Tan Bui-ThanhBayesian Inference Score-Based Generative Modeling
The Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin · The Oden Institute for Computational Engineering and Sciences, and the Department of Aerospace Engineering & Engineering Mechanics, The University of Texas at Austin
Statistical validation and full-sphere extension of a Bayesian model for human static sound localisation
Jun 23, 2026Roberto Barumerli, Fabian Brinkmann, Emanuele Zanoni +3Bayesian Inference Spatial Audio
Dyson School of Design Engineering, Imperial College London, London, United Kingdom · Audio Communication Group, Technische Universität Berlin, Germany · Department of Industrial Systems Technology and Management, University of Padova, Vicenza, Italy
Learning Dynamical Systems from Multiple Sparse Datasets: A Hierarchical Bayesian Modeling Approach
Jun 23, 2026Cristian Brugnara, Lea Multerer, Marco Forgione +1Irregular Time-Series Modeling Parameter Estimation
SUPSI, Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI), Lugano, Switzerland
Scalable Bayesian Additive Models for Stellar Flare Detection via Amortized Gaussian Process Inference and Hidden Markov Models
Jun 21, 2026Rodrigo Herrera, Vianey Leos-Barajas, Gwendolyn Eadie +2Neural Surrogate Modeling Amortized Inference
Department of Statistical Sciences, University of Toronto · Data Sciences Institute, University of Toronto · School of the Environment, University of Toronto +3
BLENDS: Bayesian Learning-Enhanced Deep Smoothing for GNSS-Denied Environments
Jun 21, 2026Nadav Cohen, Itzik KleinBayesian Inference GNSS-Denied Localization
The Hatter Department of Marine Technologies University of Haifa Haifa, Israel
Neural Conjugate Aggregation: Identifiable Unsupervised Multi-Sensor Regression under Heterogeneous Sensor Bias
Jun 20, 2026Muhammed Faruk Aytin, Zehra Demir, Alper Unal +2Bayesian Inference Uncertainty Calibration
AI and Data Engineering Department, Istanbul Technical University, Istanbul, Türkiye · Eurasia Institute of Earth Sciences, Istanbul Technical University, Istanbul, Türkiye · Civil & Environmental Engineering, University of Washington, Seattle, WA, USA
When Does Belief-Based Agent Memory Help? Reliability-Conditional Updating and Provenance-Capped Poisoning Defense
Jun 20, 2026Pranav SinghAgent Memory Bayesian Inference
Indian Institute of Technology Ropar
Lightweight 3D Feature Pretraining by Bayesian Inversion of 2D Foundation Models
Jun 19, 2026Marwane Hariat, Gianni Franchi, David Filliat +1Multi-View Consistency Bayesian Inference
U2IS, ENSTA – Institut Polytechnique de Paris, Palaiseau, France · Pôle Recherche, Agence Ministérielle pour l’IA de Défense, Palaiseau, France
Bayesian Model Averaging under Predictor Redundancy via Density-Ratio Posterior Compression
Jun 19, 2026Hanqing Li, Xuewen Lu, Yuting ChenBayesian Inference
Department of Mathematics and Statistics University of Calgary Calgary, AB T2N 1N4, Canada · Department of Mathematics and Statistics Eastern Kentucky University Richmond, KY 40475, USA
Multi-Task Bayesian In-Context Learning
Jun 18, 2026Qingyang Zhu, Eric Karl Oermann, Kyunghyun ChoAmortized Inference Bayesian Inference
New York University · NYU Langone Health
Variational Consensus Monte Carlo for Bayesian Mixture
Jun 17, 2026Julie Fendler, Francesca L. Crowe, Tom Marshall +2Markov Chain Monte Carlo Federated Learning Aggregation
MRC Biostatistics Unit, University of Cambridge · Institute of Applied Health Research, University of Birmingham
Structured Inference with Large Language Gibbs
Jun 17, 2026Sanghyeok Choi, Henry Gouk, Esmeralda S. WhitammerMarkov Chain Monte Carlo LLM Inference
University of Edinburgh, School of Informatics · 2CIFAR Fellow, Learning in Machines and Brains
Shrinkage priors for Bayesian Substitute Confounders
Jun 16, 2026Yordan P. Raykov, Hengrui Luo, Justin D. Strait +1Causal Effect Estimation Latent Variable Models
School of Mathematical Sciences, University of Nottingham, Nottingham, UK · Department of Statistics, Rice University, USA · Lawrence Berkeley National Laboratory, USA +1

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Nonparametric Bayesian Inverse Reinforcement Learning with Data-Parallel Gibbs Sampling

Sai Anirudh Katupilla, Shreeya Dasa Lakshminath

University of Maryland, College Park

Reinforcement Learning Bayesian Inference

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Inverse Reinforcement Learning recovers reward functions from expert demonstrations, but standard formulations assume that all demonstrations come from a single expert. When demonstrations are pooled from multiple experts with distinct preferences, parametric methods recover an averaged reward that fits no individual expert well. We implement Nonparametric Bayesian Inverse Reinforcement Learning with a Dirichlet Process prior over reward functions, allowing the number of latent reward types to be inferred jointly with the rewards themselves. Inference uses a collapsed Gibbs sampler combining a Chinese Restaurant Process update for cluster assignments with a Metropolis-Hastings update for reward weights, and soft value iteration as the inner planning routine. We evaluate on a 10x10 ObjectWorld grid with two and three ground-truth reward types. The serial sampler recovers K=2 with Adjusted Rand Index of 1.000, substantially outperforming a Maximum Entropy IRL baseline (ARI=0.000). Extension to K=3 shows that the sampler correctly identifies the number of clusters in all runs; assignment ARI of 0.48-0.58 reflects behavioral overlap between expert types that persists across grid instantiations, revealing that reliable K=3 evaluation on ObjectWorld requires controlled object placement rather than random seeding. We further parallelize the sampler across CPU cores using Ray on HPC hardware, achieving a peak speedup of 4.79x at 8 workers, and characterize a throughput-versus-accuracy tradeoff arising from the consensus merge heuristic used during state aggregation. Code and a containerized environment are available at https://github.com/dasashreeya/np_bayes_irl.

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