Hierarchical Bayesian Modeling

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3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Amortized Bayesian Inference on Multilevel Models of Arbitrary Structure
Sep 30, 2026Daniel Habermann, Andreas Bulling, Stefan T. Radev +1Amortized Inference Hierarchical Bayesian Modeling
Department of Statistics TU Dortmund University, Germany · Institute for Visualisation and Interactive Systems University of Stuttgart, Germany · Department of Cognitive Science Rensselaer Polytechnic Institute, USA
Prior-Amortized In-Context Bayesian Inference for Generalized Linear Mixed-Effects Models
Sep 21, 2026Alex Kipnis, Marcel Binz, Eric SchulzNeural Posterior Estimation Amortized Inference
Institute for Human-Centered AI, Helmholtz Munich, Germany
Diffusion Models and Concept Formation
Sep 11, 2026Zekun Wang, Karthik Singaravadivelan, Christopher J. MacLellanCognitive Modeling Hierarchical Representation Learning
College of Computing, Georgia Institute of Technology, Atlanta, GA 30332 USA
Hierarchical Empirical-Bayes Naive Bayes: Minimax Smoothing and Calibration with AODE Extension
Aug 11, 2026Nguyen Thai Anh, Truong Viet Vu, Tran Thien Thanh +2Probability Calibration Empirical Bayes
Faculty of Information Technology, Van Lang School of Technology, Van Lang University, Ho Chi Minh City, 70000, Vietnam · Institute of Information Technology and Electrical-Electronics Engineering, Ho Chi Minh City University of Transport, Ho Chi Minh City, 700000, Vietnam
Hierarchical Solomonoff Induction: An Unbounded Machine Learning Model
Aug 2, 2026Nathan YoungHierarchical Bayesian Modeling Statistical Learning Theory
Strong AI Lab, University of Auckland
Recursive Gaussian Processes and the Bayesian Brain
Aug 1, 2026Moumita Das, Dipanjan Ray, Sourabh BhattacharyaComputational Cognitive Modeling Predictive Coding
1Indian Institute of Management, Udaipur, India · Department of Psychology and Cognitive Sciences, Ashoka University, Haryana, India · 3Koita Centre for Digital Health at Ashoka (KCDH-A), Haryana, India +1
Modeling Memory-Dependent Reliability of LLMs: A Hidden Markov Model
Jul 24, 2026Robab Aghazadeh Chakherlou, Siddartha Khastgir, Peter Popov +1LLM Evaluation Hierarchical Bayesian Modeling
WMG, University of Warwick, Coventry, United Kingdom · Centre for Software Reliability, City St George’s, University of London, London, United Kingdom
Transit Destination Inference from Tap-In-Only Bus Smart-Card Data: A Hierarchical Bayesian Approach
Jul 23, 2026Gefei Zhao, Jiahe Ling, Yuelong SuIntelligent Transportation Systems Traffic Flow Estimation
Department of Computer Science University of Wisconsin–Madison, Madison, WI, 53706 · Department of Industrial Engineering and Operations Research Columbia University, New York, NY, 10027 · State Key Laboratory of Intelligent GreenVehicle and Mobility Tsinghua University, Beijing, China, 100084
A Bayesian framework for the uncanny valley in humanoid robot design
Jul 7, 2026Shimon Honda, Rin Shibano, Hideyoshi YanagisawaHuman Preference Modeling Hierarchical Bayesian Modeling
Department of Mechanical Engineering, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan.
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
Estimating Supply Incrementality in Two-sided Marketplaces: A Causal Machine Learning Approach
Jun 30, 2026Yufei Wu, Daniel Schmierer, Dan ZylberglejdCausal Effect Estimation Debiased ML
Airbnb, Inc. San Francisco, California, USA
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
Multi-Task Bayesian In-Context Learning
Jun 18, 2026Qingyang Zhu, Eric Karl Oermann, Kyunghyun ChoAmortized Inference Bayesian Inference
New York University · NYU Langone Health
Credibility-Weighted Pricing of Autonomous Vehicle Liability Under Operational Design Domain Shift
Jun 16, 2026Doyeon JangBayesian Inference Autonomous Driving
Doyeon Jang
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
DTVEM-RE: A Hierarchical Random-Effects Extension of the Differential Time-Varying Effect Model for Person-Specific Multi-Lag Estimation in Intensive Longitudinal Data
Jun 12, 2026Amartya BhattacharyaIrregular Time-Series Modeling Hierarchical Bayesian Modeling
Geisel School of Medicine, Dartmouth College
Hierarchical Projection for Adaptive Knowledge Transfer
Jun 7, 2026Samhita Pal, Tian GuHierarchical Bayesian Modeling Cross-Domain Transfer Learning
Vanderbilt University Medical Center · Columbia University
Corrected Integrated Laplace Approximation for Bayesian Inference in Latent Gaussian Models
May 19, 2026Jinlin Lai, Charles C. Margossian, Daniel R. SheldonLatent Variable Models Importance Sampling
Manning College of Information and Computer Sciences University of Massachusetts Amherst · Department of Statistics University of British Columbia
Bayesian Nonparametric Mixed-Effect ODEs with Gaussian Processes
May 13, 2026Julien Martinelli, Maksim Sinelnikov, Harri Lähdesmäki +2Dynamical Systems Bayesian Inference
Aalto University · Inria SISTM team · Univ. Bordeaux, INSERM BPH, U1219, Inria SISTM team, VRI, France
Correcting Selection Bias in Sparse User Feedback for Large Language Model Quality Estimation: A Multi-Agent Hierarchical Bayesian Approach
May 12, 2026Andrea Morandi, Mahesh ViswanathanLLM Evaluation Bayesian Inference
Cisco Systems, Inc.
Two Ways to De-Bias an LLM-as-a-Judge: A Continuous-Score Comparison of Hierarchical Bayesian Calibration and Neural-ODE Score Transport
May 9, 2026Andrea MorandiPost-Hoc Calibration LLM-as-a-Judge
Cisco Systems, Inc.
Robust volatility updates for Hierarchical Gaussian Filtering
May 1, 2026Christoph Mathys, Nicolas Legrand, Peter Thestrup Waade +2Bayesian Filtering Hierarchical Bayesian Modeling
Interacting Minds Centre, Aarhus University, 8000 Aarhus C, Denmark · Centre for Humanities Computing, Aarhus University, 8000 Aarhus C, Denmark · Translational Neuromodeling Unit, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland +1
Tokenised Flow Matching for Hierarchical Simulation Based Inference
Apr 22, 2026Giovanni Charles, Cosmo Santoni, Seth Flaxman +1Flow Matching Simulation-Based Inference
Imperial College London, London, United Kingdom · University of Oxford, Oxford, United Kingdom
Deep probabilistic model synthesis enables unified modeling of whole-brain neural activity across individual subjects
Mar 15, 2026William E. Bishop, Luuk W. Hesselink, Bernhard Englitz +2Bayesian Inference Hierarchical Bayesian Modeling
Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA · Computational Neuroscience Lab, Donders Center for Neuroscience, Radboud University, Nijmegen, The Netherlands · Departments of Neurobiology, Engineering Sciences and Applied Mathematics, and Physics and Astronomy, Northwestern University, Evanston, IL +1
Hierarchical Sparse Bayesian Multitask Learning for Disease Prediction in Pooled Microbiome Studies
Feb 4, 2025Haonan Zhu, Andre R. Goncalves, Camilo Valdes +10Clinical Outcome Prediction Multi-Task Learning
Lawrence Livermore National Laboratory · Electrical and Computer Engineering, University of Michigan
Hierarchical Bayesian Crowdsourcing with Item Difficulty
May 29, 2024Seong Woo Han, Ozan Adıgüzel, Bob CarpenterInter-Rater Reliability Latent Variable Models
University of Pennsylvania · Columbia University · Center for Computational Mathematics, Flatiron Institute

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Amortized Bayesian Inference on Multilevel Models of Arbitrary Structure

Daniel Habermann, Andreas Bulling, Stefan T. Radev, Paul-Christian Bürkner

Department of Statistics TU Dortmund University, Germany · Institute for Visualisation and Interactive Systems University of Stuttgart, Germany · Department of Cognitive Science Rensselaer Polytechnic Institute, USA

Amortized Inference Hierarchical Bayesian Modeling Probabilistic Graphical Models

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We develop a general method for amortized Bayesian inference on multilevel models of arbitrary structure. Given a generative model specified as a directed acyclic graph, our method automatically derives valid factorizations of the joint posterior and matching neural network architectures. The key steps, graph expansion and graph inversion, yield an inverse graph that determines how inference networks are stacked and conditioned, producing factorizations that amortize over the number of groups and the number of observations within each group. Unlike approaches that simplify the dependency structure to speed up learning or inference, our method preserves all conditional independence and exchangeability assumptions of the generative model. Across three case studies, it closely matches gold-standard samplers on models with more than 6,500 parameters while reducing inference to a near-instant forward pass once trained.

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