Dynamic Bayesian knowledge transfer between a pair of Kalman filters

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F18%3A00499667" target="_blank" >RIV/67985556:_____/18:00499667 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/MLSP.2018.8517020" target="_blank" >http://dx.doi.org/10.1109/MLSP.2018.8517020</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/MLSP.2018.8517020" target="_blank" >10.1109/MLSP.2018.8517020</a>

Result language
angličtina
Original language name
Dynamic Bayesian knowledge transfer between a pair of Kalman filters
Original language description
Transfer learning is a framework that includes---among other topics---the design of knowledge transfer mechanisms between Bayesian filters. Transfer learning strategies in this context typically rely on a complete stochastic dependence structure being specified between the participating learning procedures (filters). This paper proposes a method that does not require such a restrictive assumption. The solution in this incomplete modelling case is based on the fully probabilistic design of an unknown probability distribution which conditions on knowledge in the form of an externally supplied distribution. We are specifically interested in the situation where the external distribution accumulates knowledge dynamically via Kalman filtering. Simulations illustrate that the proposed algorithm outperforms alternative methods for transferring this dynamic knowledge from the external Kalman filter.
Czech name
—
Czech description
—

Project
<a href="/en/project/GA18-15970S" target="_blank" >GA18-15970S: Optimal Distributional Design for External Stochastic Knowledge Processing</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
PROCEEDINGS OF MLSP 2018 : IEEE 28th International Workshop on Machine Learning for Signal Processing
ISBN
978-1-5386-5478-1
ISSN
1551-2541
e-ISSN
—
Number of pages
6
Pages from-to
—
Publisher name
IEEE
Place of publication
Piscataway
Event location
Aalborg
Event date
Sep 17, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000450651000042

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