Dynamic Online Processor-Native Inference for State Estimation

• URL: http://arxiv.org/abs/2607.12095v1
• Date: Mon, 13 Jul 2026 19:13:45 GMT
• Title: Dynamic Online Processor-Native Inference for State Estimation
• Abstract summary: Sensor-rich data-driven applications increasingly use Bayesian approaches to infer latent states of dynamic systems from noisy sensor measurements and physical models.<n>This paper presents a Bayesian filtering technique that uses processor-native uncertainty tracking for both uncertainty propagation and inference.<n> Benchmarks across three nonlinear state-space systems compare the approach against particle filters and Monte-Carlo-based likelihood estimators.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sensor-rich data-driven applications increasingly use Bayesian approaches to infer latent states of dynamic systems from noisy sensor measurements and physical models. Yet the computation of the likelihood remains an essential bottleneck for accurate posteriors and performant inference. This paper presents a Bayesian filtering technique that uses processor-native uncertainty tracking for both uncertainty propagation and inference. The technique implements deterministic hierarchical importance restructuring through a native operation, giving deterministic latency and bounded memory use for arbitrary models written as program code. Benchmarks across three nonlinear state-space systems compare the approach against particle filters and Monte-Carlo-based likelihood estimators. The technique enables deterministic approximate filtering with as high as 805$\times$ average speedup against direct Monte Carlo work at matched result quality for model evaluation, and Pareto-dominant accuracy-latency trade-offs for posterior inference while remaining competitive in RMSE with baseline particle filters.

Related papers

• 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)
• Assumed Density Filtering and Smoothing with Neural Network Surrogate Models [0.0]
  We show that cross entropy is a more appropriate performance metric than RMSE for evaluating the accuracy of filter and smoothers.<n>We demonstrate the superiority of our method for state estimation on a Lorenz system and a Wiener system, and find that our method enables more optimal linear regulation when the state estimate is used for feedback.
  arXiv  Detail & Related papers  (2025-11-12T06:08:53Z)
• Robust Filtering -- Novel Statistical Learning and Inference Algorithms with Applications [0.0]
  State estimation serves as a fundamental task to enable intelligent decision-making in applications such as autonomous vehicles, robotics, healthcare monitoring, smart grids, intelligent transportation, and predictive maintenance.<n>Standard filtering assumes prior knowledge of noise statistics to extract latent system states from noisy sensor data.<n>Real-world scenarios involve abnormalities like outliers, biases, drifts, and missing observations with unknown or partially known statistics, limiting conventional approaches.<n>This thesis presents novel robust nonlinear filtering methods to mitigate these challenges.
  arXiv  Detail & Related papers  (2025-06-13T07:30:35Z)
• Adaptive Bayesian Optimization for Robust Identification of Stochastic Dynamical Systems [4.0148499400442095]
  This paper deals with the identification of linear derivation systems, where the unknowns include system coefficients and noise variances.<n>A sample-efficient global optimization method based on Bayesian optimization is proposed.<n>Experiments show that the EGP-based BO consistently outperforms MLE via steady-state filtering and expectation-maximization.
  arXiv  Detail & Related papers  (2025-03-09T01:38:21Z)
• Semi-Supervised Model-Free Bayesian State Estimation from Compressed Measurements [57.04370580292727]
  We consider data-driven Bayesian state estimation from compressed measurements.<n>The dimension of the temporal measurement vector is lower than that of the temporal state vector to be estimated.<n>The underlying dynamical model of the state's evolution is unknown for a'model-free process'
  arXiv  Detail & Related papers  (2024-07-10T05:03:48Z)
• Outlier-robust Kalman Filtering through Generalised Bayes [45.51425214486509]
  We derive a novel, provably robust, and closed-form Bayesian update rule for online filtering in state-space models. Our method matches or outperforms other robust filtering methods at a much lower computational cost.
  arXiv  Detail & Related papers  (2024-05-09T09:40:56Z)
• Closed-form Filtering for Non-linear Systems [83.91296397912218]
  We propose a new class of filters based on Gaussian PSD Models, which offer several advantages in terms of density approximation and computational efficiency. We show that filtering can be efficiently performed in closed form when transitions and observations are Gaussian PSD Models. Our proposed estimator enjoys strong theoretical guarantees, with estimation error that depends on the quality of the approximation and is adaptive to the regularity of the transition probabilities.
  arXiv  Detail & Related papers  (2024-02-15T08:51:49Z)
• Online Variational Sequential Monte Carlo [49.97673761305336]
  We build upon the variational sequential Monte Carlo (VSMC) method, which provides computationally efficient and accurate model parameter estimation and Bayesian latent-state inference. Online VSMC is capable of performing efficiently, entirely on-the-fly, both parameter estimation and particle proposal adaptation.
  arXiv  Detail & Related papers  (2023-12-19T21:45:38Z)
• 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)
• Low-rank extended Kalman filtering for online learning of neural networks from streaming data [71.97861600347959]
  We propose an efficient online approximate Bayesian inference algorithm for estimating the parameters of a nonlinear function from a potentially non-stationary data stream. The method is based on the extended Kalman filter (EKF), but uses a novel low-rank plus diagonal decomposition of the posterior matrix. In contrast to methods based on variational inference, our method is fully deterministic, and does not require step-size tuning.
  arXiv  Detail & Related papers  (2023-05-31T03:48:49Z)
• Deep Variational Models for Collaborative Filtering-based Recommender Systems [63.995130144110156]
  Deep learning provides accurate collaborative filtering models to improve recommender system results. Our proposed models apply the variational concept to injectity in the latent space of the deep architecture. Results show the superiority of the proposed approach in scenarios where the variational enrichment exceeds the injected noise effect.
  arXiv  Detail & Related papers  (2021-07-27T08:59:39Z)
• Tracking Performance of Online Stochastic Learners [57.14673504239551]
  Online algorithms are popular in large-scale learning settings due to their ability to compute updates on the fly, without the need to store and process data in large batches. When a constant step-size is used, these algorithms also have the ability to adapt to drifts in problem parameters, such as data or model properties, and track the optimal solution with reasonable accuracy. We establish a link between steady-state performance derived under stationarity assumptions and the tracking performance of online learners under random walk models.
  arXiv  Detail & Related papers  (2020-04-04T14:16:27Z)

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.