Bayesian Filtering

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3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Latest papers 19

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Improving Ensemble Filters with Flow Matching
Sep 23, 2026Haoyuan Chen, Alexandre ThiéryData Assimilation Bayesian Filtering
Department of Statistics and Data Science National University of Singapore 6 Science Drive 2, Singapore 117546
Bayesian Filtering in Physical Systems via Test-time Trained Flow Matching
Sep 20, 2026Ruiqi Feng, Chongyi Wang, Tao Zhang +1Bayesian Filtering Conditional Flow Matching
Westlake University & Zhejiang University · Westlake University
Generative models for simulation based filtering: Formulations and Empirical Comparisons
Sep 14, 2026Mohammad Al-Jarrah, Wei Deng, Bamdad Hosseini +1Bayesian Filtering Particle Filters
Department of Aeronautics & Astronautics, University of Washington, Seattle · Department of Applied Mathematics, University of Washington, Seattle · ML Research, Morgan Stanley
Dynamic Online Processor-Native Inference for State Estimation
Jul 13, 2026Orestis KaparounakisBayesian Filtering Particle Filters
Tensorized algorithms and scalable filtering methods for hidden Markov and factorial hidden Markov models
Jul 8, 2026Roxana Barrios, Ioannis SgouralisHidden Markov Models Tensor Decomposition
Department of Mathematics, University of Tennessee, Knoxville, TN
A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements
Jun 30, 2026Kundan Kumar, Shreya Das, Simo SärkkäBayesian Filtering Lagrangian Methods
Learning Probabilistic Filters with Strictly Proper Scoring Rules
Jun 25, 2026Eviatar Bach, Ricardo Baptista, Jochen Bröcker +2Bayesian Filtering Data Assimilation
Department of Meteorology, University of Reading Brian Hoskins Building, Reading, RG6 6ET, UK · Department of Mathematics and Statistics, University of Reading Pepper Lane, Reading, RG6 6AX, UK · National Centre for Earth Observation Brian Hoskins Building, University of Reading, Reading, RG6 6ET, UK +5
Invariant Kalman filtering for extended pose estimation in multi-IMU articulated rigid-body systems
Jun 23, 2026Sven Goffin, Cédric Schwartz, Silvère Bonnabel +2Extended Kalman Filter Category-Level Object Pose Estimation
Department of Electrical Engineering and Computer Science, University of Liège, Belgium · Department of Physical Activity and Rehabilitation Sciences, University of Liège, Belgium · Department of Mathematics and Systems, Mines Paris – PSL, France +1
Invariant Stochastic Filtering on SE(3) for Inertial-Encoder State Estimation of Serial Rigid Manipulators
Jun 21, 2026S. Yaqubi, J. MattilaExtended Kalman Filter State Estimation
Department of Automation Technology and Mechanical Engineering, Tampere University, Korkeakoulunkatu 6, 33720 Tampere, Finland
Structured Noise Adaptation for Sequential Bayesian Filtering with Embedded Latent Transfer Operators
Jun 12, 2026Naichang Ke, Pongpisit Thanasutives, Yoshinobu KawaharaBayesian Filtering State Estimation
The University of Osaka · RIKEN Center for Advanced Intelligence Project (AIP)
Training-Free Bayesian Filtering with Generative Emulators
May 19, 2026Thomas Savary, François Rozet, Gilles LouppeBayesian Filtering Particle Filters
SAIL, Montefiore institute, University of Liège, Belgium.
The Score Kalman Filter
May 15, 2026Kaito Iwasaki, Anthony Bloch, Taeyoung Lee +1Bayesian Filtering Extended Kalman Filter
Department of Mathematics University of Michigan · Department of Mechanical and Aerospace Engineering George Washington University · Department of Naval Architecture & Marine Engineering and Department of Robotics University of Michigan
Coupling-Informed Transport Maps for Bayesian Filtering in Nonlinear Dynamical Systems
May 13, 2026Dengfei Zeng, Lijian Jiang, Shuyu Sun +1Bayesian Filtering Posterior Sampling
School of Mathematical Sciences, Tongji University, Shanghai, 200092 China · School of Mathematical Sciences, Tongji University, Shanghai, 200092 China, Key Laboratory of Intelligent Computing and Applications (Ministry of Education), Tongji University, Shanghai, 200092 China
Precision Tracked Transformer via Kalman Filtering, Kriging and Process Noise
May 12, 2026Bo Long, Deepak Agarwal, Jelena Markovic-Voronov +2Transformer Architectures Bayesian Filtering
LinkedIn Core AI
SAFformer:Improving Spiking Transformer via Active Predictive Filtering
May 8, 2026Zequan Xie, Weiming Zeng, Yunhua Chen +3Spiking Neural Networks Transformer Architectures
School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China · Faculty of Science, Hong Kong Baptist University, China · School of Electronic Information, Wuhan University, China
Understanding Self-Supervised Learning via Latent Distribution Matching
May 5, 2026Fabian A Mikulasch, Friedemann ZenkeFuture Latent Representations Bayesian Filtering
Friedrich Miescher Institute for Biomedical Research, 4056 Basel, Switzerland · Faculty of Science, University of Basel, 4033 Basel, Switzerland
Online Generalised Predictive Coding
May 4, 2026Mehran H. Z. Bazargani, Szymon Urbas, Adeel Razi +2Predictive Coding Data Assimilation
Insight Research Ireland Centre, UCD, Dublin, Ireland. · UCD School of Mathematics and Statistics, UCD, Belfield, Dublin, Ireland. · Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia. +5
Natural Gradient Bayesian Filtering: Geometry-Aware Filter for Dynamical Systems
May 4, 2026Chang Liu, Wenhan Cao, Zeju Sun +8Bayesian Filtering Extended Kalman Filter
School of Advanced Manufacturing and Robotics, Peking University, China. · School of Vehicle and Mobility, Tsinghua University, China. · Beijing Institute of Mathematical Sciences and Applications (BIMSA), China. +3
Efficient Online 3D Multi-Camera Multi-Object Tracking and Pose Estimation
Apr 16, 2026Linh Van Ma, Tran Thien Dat Nguyen, Juhua Hu +2Multi-Object Tracking Monocular
School of Electrical Engineering and Computer Science at GIST, Gwangju, Korea · School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Australia · School of Engineering and Technology, University of Washington, USA

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Improving Ensemble Filters with Flow Matching

Haoyuan Chen, Alexandre Thiéry

Department of Statistics and Data Science National University of Singapore 6 Science Drive 2, Singapore 117546

Data Assimilation Bayesian Filtering Ensemble Covariance

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Data assimilation estimates a dynamical state from partial and noisy observations. Classical ensemble filters are efficient but restrict analysis updates through finite sample covariance and affine Gaussian distribution. We introduce the Flow Ensemble Filter (FlowEF), which uses conditional flow matching to transport the forecast ensemble from a classical baseline filter to an analysis ensemble. FlowEF uses a localized Gaussian source during training, transports forecast ensemble members from a baseline filter at deployment, and conditions its velocity field on ensembles from that baseline filter and the observation. The proposed model therefore learns a nonlinear update while mapping each baseline ensemble independently. For sparsely observed dynamical systems, FlowEF improves both deterministic and probabilistic metrics over all four classical ensemble filters. It also achieves the best performance among the state-of-the-art generative data assimilation models.

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