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Conference Paper (international conference)

Dynamic Bayesian knowledge transfer between a pair of Kalman filters

Papež Milan, Quinn Anthony

: PROCEEDINGS OF MLSP 2018 : IEEE 28th International Workshop on Machine Learning for Signal Processing

: International Workshop on Machine Learning for Signal Processing 2018 (MLSP 2018) /28./, (Aalborg, DK, 20180917)

: GA18-15970S, GA ČR

: Bayesian transfer learning, Fully probabilistic design, Kalman filtering

: 10.1109/MLSP.2018.8517020

: http://library.utia.cas.cz/separaty/2019/AS/papez-0499667.pdf

(eng): 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.

: BB

: 10103