2026-07-24 04:00 UTCOriginal source2 min readUpdated: 2026-07-24 08:27 UTC

Generative Bayesian Filtering for State Estimation

This paper proposes Generative Bayesian Filtering (GBF), a filtering framework that replaces restrictive observation models with pretrained conditional generative models (CVAE). GBF performs Bayesian prediction-update recursion where the measurement update is a posterior sampling problem combining dynamical prior with CVAE-induced likelihood, transformed into a score-based sampling problem. Experiments on synthetic data and real-world applications like manufacturing monitoring and arrhythmia diagnosis show improved accuracy and robustness.

SourcearXiv Machine LearningAuthor: Lei Cao, Sihang Feng, Jixin Yan, Tao Sun, Naichen Shi

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Key points

GBF uses conditional variational autoencoders (CVAE) to replace traditional linear-Gaussian observation models.
The measurement update in Bayesian recursion is formulated as a posterior sampling problem combining dynamical prior and CVAE likelihood.
The filtering problem is transformed into a score-based sampling problem, inheriting generative model flexibility and ensemble uncertainty quantification.
Experiments on manufacturing system monitoring and arrhythmia diagnosis demonstrate improved state estimation accuracy and robustness.

Why it matters

This matters because GBF uses conditional variational autoencoders (CVAE) to replace traditional linear-Gaussian observation models.

Technical impact

May affect model selection, inference cost, product capability, and evaluation benchmarks.

This panel is AI-generated and reviewed for accuracy.

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[Submitted on 9 Jul 2026]

Title:Generative Bayesian Filtering for State Estimation

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Abstract:The state of a dynamic system evolves over time, switching among several latent modes that govern its observable behavior. Filtering methods infer the latent state from observations. Classical filtering approaches, including Kalman filters, typically rely on simple observation models, such as linear-Gaussian models, that are incapable of characterizing the increasingly nonlinear and heterogeneous patterns in high-dimensional sensor signals. To tackle the challenge, we propose Generative Bayesian Filtering (GBF), a filtering framework that replaces restrictive observation models with pretrained conditional generative models parametrized by conditional variational autoencoders (CVAE). For online inference, GBF performs a Bayesian prediction-update recursion in which the measurement update is formulated as a posterior sampling problem that combines the dynamical prior with the CVAE-induced likelihood. The resulting filtering problem is then transformed into a score-based sampling problem, which naturally inherits the flexibility from generative models and the uncertainty quantification capabilities from ensembling. Experiments on synthetic datasets and real-world applications involving manufacturing system monitoring and arrhythmia diagnosis demonstrate that GBF improves state estimation accuracy and robustness relative to baseline approaches.

Subjects:

Machine Learning (cs.LG); Machine Learning (stat.ML)

Cite as: arXiv:2607.20521 [cs.LG]

(or arXiv:2607.20521v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.20521

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Lei Cao [view email] [v1] Thu, 9 Jul 2026 00:05:23 UTC (1,201 KB)

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