Knowledge Transfer in a Pair of Uniformly Modelled Bayesian Filters

0507278 - ÚTIA 2020 RIV PT eng C - Konferenční příspěvek (zahraniční konf.)
Jirsa, Ladislav - Pavelková, Lenka - Quinn, Anthony
Knowledge Transfer in a Pair of Uniformly Modelled Bayesian Filters.
Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2019). Setubal: SCITEPRESS – Science and Technology Publications, Lda, 2019 - (Gusikhin, O.; Madani, K.; Zaytoon, J.), č. článku 50. ISBN 978-989-758-380-3. ISSN 2184-2809.
[International Conference on Informatics in Control, Automation and Robotics (ICINCO 2019) /16./. Prague (CZ), 29.07.2019-31.07.2019]
Grant CEP: GA ČR(CZ) GA18-15970S
Institucionální podpora: RVO:67985556
Klíčová slova: Fully Probabilistic Design * Bayesian Filtering * Uniform Noise * Knowledge Transfer * Predictor * Orthotopic Bounds
Obor OECD: Statistics and probability
http://library.utia.cas.cz/separaty/2019/AS/jirsa-0507278.pdf

The paper presents an optimal Bayesian transfer learning technique applied to a pair of linear state-space processes driven by uniform state and observation noise processes. Contrary to conventional geometric approaches to boundedness in filtering problems, a fully Bayesian solution is adopted. This provides an approximate uniform filtering distribution and associated data predictor by processing the involved bounds via a local uniform approximation. This Bayesian handling of boundedness provides the opportunity to achieve optimal Bayesian knowledge transfer between bounded-error filtering nodes. The paper reports excellent rejection of knowledge below threshold, and positive transfer above threshold. In particular, an informal variant achieves strong transfer in this latter regime, and the paper discusses the factors which may influence the strength of this transfer.

Trvalý link: http://hdl.handle.net/11104/0298578