Research Paper#Dynamical Systems, Bayesian Inference, Parameter Estimation, Uncertainty Quantification🔬 ResearchAnalyzed: Jan 3, 2026 06:11
Online Parameter-State Estimation with Uncertainty Quantification via Variational Inference
Published:Dec 31, 2025 18:52
•1 min read
•ArXiv
Analysis
This paper addresses the critical problem of online joint estimation of parameters and states in dynamical systems, crucial for applications like digital twins. It proposes a computationally efficient variational inference framework to approximate the intractable joint posterior distribution, enabling uncertainty quantification. The method's effectiveness is demonstrated through numerical experiments, showing its accuracy, robustness, and scalability compared to existing methods.
Key Takeaways
- •Proposes a variational inference framework for online parameter-state estimation.
- •Provides uncertainty quantification through approximation of the joint posterior.
- •Demonstrates accuracy, robustness, and scalability through numerical experiments.
- •Outperforms existing methods in certain scenarios.
Reference
“The paper presents an online variational inference framework to compute its approximation at each time step.”