Structured Noise Adaptation for Sequential Bayesian Filtering with Embedded Latent Transfer Operators

• URL: http://arxiv.org/abs/2606.14195v1
• Date: Fri, 12 Jun 2026 07:28:54 GMT
• Title: Structured Noise Adaptation for Sequential Bayesian Filtering with Embedded Latent Transfer Operators
• Abstract summary: We introduce an ELTO-based Bayesian filtering approach with a new structured parameterization for the filter's noise model.<n>Our results show that our structured noise adaptation improves the filter's dynamic state estimation performance in noisy, time-varying environments.
• Score: 4.876446735726356
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Kalman filters based on the Embedded Latent Transfer Operators (ELTO) emerge as novel statistical tools for sequential state estimation. However, a critical limitation stems from their use of simplified noise models, which fail to dynamically adapt to non-stationary processes. To address this limitation, we introduce an ELTO-based Bayesian filtering approach with a new structured parameterization for the filter's noise model. This parameterization enables structured noise adaptation, which couples the data-driven learning of an optimal time-invariant noise model with dynamic parameter adaptation that responds to changes in dynamics within non-stationary processes. Empirical results show that our structured noise adaptation improves the filter's dynamic state estimation performance in noisy, time-varying environments.

Related papers

• Equilibrium Forcing: Adaptive Video Generation Without Noise Conditioning [85.93126001283169]
  We introduce Equilibrium Forcing (EqF), a framework for video denoising generative models without noise level conditioning.<n>This flexibility allows for inference-time algorithms that operate in a closed loop by adapting to feedback from the sample.
  arXiv  Detail & Related papers  (2026-08-11T00:02:32Z)
• Conditional Normalizing Flows for Forward and Backward Joint State and Parameter Estimation [0.0]
  This study reviews recent approaches to normalizing state estimation via nonlinear filtering.<n>We investigate the performance of these approaches on applications relevant to autonomous driving and patient population dynamics.<n>Finally, we assess the performance of various conditioning strategies for an application to real-world COVID-19 joint SIR system forecasting and estimation.
  arXiv  Detail & Related papers  (2026-01-11T18:01:42Z)
• TS-DP: Reinforcement Speculative Decoding For Temporal Adaptive Diffusion Policy Acceleration [64.32072516882947]
  Diffusion Policy excels in embodied control but suffers from high inference latency and computational cost.<n>We propose Temporal-aware Reinforcement-based Speculative Diffusion Policy (TS-DP)<n>TS-DP achieves up to 4.17 times faster inference with over 94% accepted drafts, reaching an inference frequency of 25 Hz.
  arXiv  Detail & Related papers  (2025-12-13T07:53:14Z)
• Addressing prior dependence in hierarchical Bayesian modeling for PTA data analysis II: Noise and SGWB inference through parameter decorrelation [0.0]
  PTA analyses assign fixed uniform noise priors to each pulsar, an approach that can introduce systematic biases when combining the array.<n>We adopt a hierarchical Bayesian modeling strategy in which noise priors are parametrized by higher-level hyper parameters.<n>We show that the hierarchical treatment constrains the noise parameters more tightly and partially alleviates the red-noise-SGWB degeneracy.
  arXiv  Detail & Related papers  (2025-11-03T17:54:55Z)
• Mitigating the Noise Shift for Denoising Generative Models via Noise Awareness Guidance [54.88271057438763]
  Noise Awareness Guidance (NAG) is a correction method that explicitly steers sampling trajectories to remain consistent with the pre-defined noise schedule.<n>NAG consistently mitigates noise shift and substantially improves the generation quality of mainstream diffusion models.
  arXiv  Detail & Related papers  (2025-10-14T13:31:34Z)
• Composite Reward Design in PPO-Driven Adaptive Filtering [0.0]
  This letter proposes an adaptive filtering framework using Proximal Policy Optimization (PPO), guided by a composite reward that balances SNR improvement, MSE reduction, and residual smoothness.<n> Experiments on synthetic signals with various noise types show that our PPO agent generalizes beyond its training distribution, achieving real-time performance and outperforming classical filters.
  arXiv  Detail & Related papers  (2025-05-29T23:11:48Z)
• Beyond Fixed Horizons: A Theoretical Framework for Adaptive Denoising Diffusions [1.9116784879310031]
  We introduce a new class of generative diffusion models that achieve a time-homogeneous structure for both the noising and denoising processes.<n>A key feature of the model is its adaptability to the target data, enabling a variety of downstream tasks using a pre-trained unconditional generative model.
  arXiv  Detail & Related papers  (2025-01-31T18:23:27Z)
• Joint State Estimation and Noise Identification Based on Variational Optimization [8.536356569523127]
  A novel adaptive Kalman filter method based on conjugate-computation variational inference, referred to as CVIAKF, is proposed. The effectiveness of CVIAKF is validated through synthetic and real-world datasets of maneuvering target tracking.
  arXiv  Detail & Related papers  (2023-12-15T07:47:03Z)
• Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference [47.460898983429374]
  We introduce an ensemble Kalman filter (EnKF) into the non-mean-field (NMF) variational inference framework to approximate the posterior distribution of the latent states. This novel marriage between EnKF and GPSSM not only eliminates the need for extensive parameterization in learning variational distributions, but also enables an interpretable, closed-form approximation of the evidence lower bound (ELBO) We demonstrate that the resulting EnKF-aided online algorithm embodies a principled objective function by ensuring data-fitting accuracy while incorporating model regularizations to mitigate overfitting.
  arXiv  Detail & Related papers  (2023-12-10T15:22:30Z)
• Latent Class-Conditional Noise Model [54.56899309997246]
  We introduce a Latent Class-Conditional Noise model (LCCN) to parameterize the noise transition under a Bayesian framework. We then deduce a dynamic label regression method for LCCN, whose Gibbs sampler allows us efficiently infer the latent true labels. Our approach safeguards the stable update of the noise transition, which avoids previous arbitrarily tuning from a mini-batch of samples.
  arXiv  Detail & Related papers  (2023-02-19T15:24:37Z)
• Adaptive Low-Pass Filtering using Sliding Window Gaussian Processes [71.23286211775084]
  We propose an adaptive low-pass filter based on Gaussian process regression. We show that the estimation error of the proposed method is uniformly bounded.
  arXiv  Detail & Related papers  (2021-11-05T17:06:59Z)
• Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering [53.523517926927894]
  We explore the use of exact per-sample Hessian-vector products and gradients to construct self-tuning quadratics. We prove that our model-based procedure converges in noisy gradient setting. This is an interesting step for constructing self-tuning quadratics.
  arXiv  Detail & Related papers  (2020-11-09T22:07:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.