Conditional Normalizing Flows for Forward and Backward Joint State and Parameter Estimation

arXiv:2601. 07013v2 Announce Type: replace-cross Abstract: Traditional filtering algorithms for state estimation -- such as classical Kalman filtering, unscented Kalman filtering, and particle filters -- show performance degradation when applied to nonlinear systems whose uncertainty follows arbitrary non-Gaussian, and potentially multi-modal distributions.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

• llms
• rag
• agents
• robotics
• multimodal