arXiv (All)
open access
Duality Theory for Filter Stability in Hidden Markov Models
This article is concerned with the problem of nonlinear (stochastic) filter stability for a discrete time hidden Markov model (HMM) with finite state and observation spaces. For this purpose, a comprehensive duality theory is developed whereby the dual control system is a backward stochastic differe
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Provably Optimal Reinforcement Learning under Safety Filtering#1034680
- Robust control of a class of nonlinear discrete-time systems: Design and experimental results on a real-time emulator#274184
- Certified Safety Curation: Distribution-Free Guarantees for Safe Offline Reinforcement Learning#999991
- Conditioning of solutions to the Sylvester equation#926895
- A Recursive CBF Framework for Safety under State Uncertainty#1012464
Record · ID 1325981
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive