State estimations and noise identifications with intermittent corrupted observations via Bayesian variational inference

• URL: http://arxiv.org/abs/2604.02738v1
• Date: Fri, 03 Apr 2026 05:20:49 GMT
• Title: State estimations and noise identifications with intermittent corrupted observations via Bayesian variational inference
• Abstract summary: We formulate the joint estimation of system states, noise parameters, and network reliability as a Bayesian variational inference problem.<n>We propose a novel variational Bayesian adaptive Kalman filter (VB-AKF) to approximate the joint posterior probability densities of the latent parameters.
• Score: 7.758003720510634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper focuses on the state estimation problem in distributed sensor networks, where intermittent packet dropouts, corrupted observations, and unknown noise covariances coexist. To tackle this challenge, we formulate the joint estimation of system states, noise parameters, and network reliability as a Bayesian variational inference problem, and propose a novel variational Bayesian adaptive Kalman filter (VB-AKF) to approximate the joint posterior probability densities of the latent parameters. Unlike existing AKF that separately handle missing data and measurement outliers, the proposed VB-AKF adopts a dual-mask generative model with two independent Bernoulli random variables, explicitly characterizing both observable communication losses and latent data authenticity. Additionally, the VB-AKF integrates multiple concurrent multiple observations into the adaptive filtering framework, which significantly enhances statistical identifiability. Comprehensive numerical experiments verify the effectiveness and asymptotic optimality of the proposed method, showing that both parameter identification and state estimation asymptotically converge to the theoretical optimal lower bound with the increase in the number of sensors.

Related papers

• Learning to Distributedly Estimate under Partially Known Dynamics: A Covariance-Agnostic Neural Kalman Consensus Filter [31.377504700683374]
  This paper presents a novel online distributed sensing framework, where agents collaborate and exchange information to perform latent state estimation.<n>The proposed estimator combines available partial domain knowledge with the representation capabilities of deep neural networks.<n>Experiments on linear, chaotic (z), and practical wireless tracking environments reveal that the proposed CoLoren-Agnostic Neural Kalman Consensus Filter (CA-NKCF) outperforms traditional distributed Kalman and particle filters.
  arXiv  Detail & Related papers  (2026-06-26T08:07:31Z)
• A Posterior-Predictive Variance Decomposition for Epistemic and Aleatoric Uncertainty in Wind Power Forecasting [3.68407754316989]
  This paper applies the law of total variance to the joint setting of neural network regression and Bayesian posterior approximation.<n>A wind power-specific evaluation framework is proposed to validate disentanglement without access to ground-truth uncertainty labels.<n>Across synthetic and real-world experiments, the decomposed AU and EU components respond in theoretically consistent directions.
  arXiv  Detail & Related papers  (2026-05-21T12:23:14Z)
• Variance-Reduced Diffusion Sampling via Target Score Identity [0.0]
  We study variance reduction for score estimation and diffusion-based sampling in settings where the clean (target) score is available or can be approximated.<n>We develop a plug-and-play nonparametric self-normalized importance sampling estimator compatible with standard reverse-time solvers.<n> Experiments on synthetic targets and PDE-governed inverse problems demonstrate improved sample quality for a fixed simulation budget.
  arXiv  Detail & Related papers  (2026-01-04T16:46:31Z)
• FIVA: Federated Inverse Variance Averaging for Universal CT Segmentation with Uncertainty Estimation [4.544160712377809]
  This work presents a novel federated learning approach to achieve universal segmentation across diverse abdominal CT datasets.<n>The proposed method quantifies prediction uncertainty by propagating the uncertainty from the model weights.<n> Experimental evaluations demonstrate the effectiveness of this approach in improving the quality of federated aggregation and uncertainty-weighted inference.
  arXiv  Detail & Related papers  (2025-08-08T11:34:01Z)
• Multiresolution Analysis and Statistical Thresholding on Dynamic Networks [49.09073800467438]
  ANIE (Adaptive Network Intensity Estimation) is a multi-resolution framework designed to automatically identify the time scales at which network structure evolves.<n>We show that ANIE adapts to the appropriate time resolution and is able to capture sharp structural changes while remaining robust to noise.
  arXiv  Detail & Related papers  (2025-06-01T22:55:55Z)
• In-Context Parametric Inference: Point or Distribution Estimators? [66.22308335324239]
  We show that amortized point estimators generally outperform posterior inference, though the latter remain competitive in some low-dimensional problems.<n>Our experiments indicate that amortized point estimators generally outperform posterior inference, though the latter remain competitive in some low-dimensional problems.
  arXiv  Detail & Related papers  (2025-02-17T10:00:24Z)
• Quantifying perturbation impacts for large language models [49.1574468325115]
  We introduce Distribution-Based Perturbation Analysis (DBPA), a framework that reformulates perturbation analysis as a frequentist hypothesis testing problem.<n>We demonstrate the effectiveness of DBPA in evaluating perturbation impacts, showing its versatility for perturbation analysis.
  arXiv  Detail & Related papers  (2024-12-01T16:13:09Z)
• FUSE: Fast Unified Simulation and Estimation for PDEs [11.991297011923004]
  We argue that solving both problems within the same framework can lead to consistent gains in accuracy and robustness. We present the capabilities of the proposed methodology for predicting continuous and discrete biomarkers in full-body haemodynamics simulations.
  arXiv  Detail & Related papers  (2024-05-23T13:37:26Z)
• 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)
• Towards stable real-world equation discovery with assessing differentiating quality influence [52.2980614912553]
  We propose alternatives to the commonly used finite differences-based method. We evaluate these methods in terms of applicability to problems, similar to the real ones, and their ability to ensure the convergence of equation discovery algorithms.
  arXiv  Detail & Related papers  (2023-11-09T23:32:06Z)
• Do Bayesian Variational Autoencoders Know What They Don't Know? [0.6091702876917279]
  The problem of detecting the Out-of-Distribution (OoD) inputs is paramount importance for Deep Neural Networks. It has been previously shown that even Deep Generative Models that allow estimating the density of the inputs may not be reliable. This paper investigates three approaches to inference: Markov chain Monte Carlo, Bayes gradient by Backpropagation and Weight Averaging-Gaussian.
  arXiv  Detail & Related papers  (2022-12-29T11:48:01Z)
• Sampling-free Variational Inference for Neural Networks with Multiplicative Activation Noise [51.080620762639434]
  We propose a more efficient parameterization of the posterior approximation for sampling-free variational inference. Our approach yields competitive results for standard regression problems and scales well to large-scale image classification tasks.
  arXiv  Detail & Related papers  (2021-03-15T16:16:18Z)
• NestedVAE: Isolating Common Factors via Weak Supervision [45.366986365879505]
  We identify the connection between the task of bias reduction and that of isolating factors common between domains. To isolate the common factors we combine the theory of deep latent variable models with information bottleneck theory. Two outer VAEs with shared weights attempt to reconstruct the input and infer a latent space, whilst a nested VAE attempts to reconstruct the latent representation of one image, from the latent representation of its paired image.
  arXiv  Detail & Related papers  (2020-02-26T15:49:57Z)

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.