Fully probabilistic design for knowledge fusion between Bayesian filters under uniform disturbances

0551618 - ÚTIA 2022 RIV NL eng J - Journal Article
Kuklišová Pavelková, Lenka - Jirsa, Ladislav - Quinn, Anthony
Fully probabilistic design for knowledge fusion between Bayesian filters under uniform disturbances.
Knowledge-Based System. Roč. 238, č. 1 (2022), č. článku 107879. ISSN 0950-7051. E-ISSN 1872-7409
R&D Projects: GA ČR(CZ) GA18-15970S
Institutional support: RVO:67985556
Keywords : Knowledge fusion * Bayesian transfer learning * Fully probabilistic design * State–space models * Bounded noise * Bayesian inference
OECD category: Statistics and probability
Impact factor: 8.8, year: 2022
Method of publishing: Limited access
http://library.utia.cas.cz/separaty/2022/AS/kuklisova-0551618.pdf https://www.sciencedirect.com/science/article/pii/S0950705121010388

This paper considers the problem of Bayesian transfer learning-based knowledge fusion between linear state-space processes driven by uniform state and observation noise processes. The target task conditions on probabilistic state predictor(s) supplied by the source filtering task(s) to improve its own state estimate. A joint model of the target and source(s) is not required and is not elicited. The resulting decision-making problem for choosing the optimal conditional target filtering distribution under incomplete modelling is solved via fully probabilistic design (FPD), i.e. via appropriate minimization of Kullback-Leibler divergence (KLD). The resulting FPD-optimal target learner is robust, in the sense that it can reject poor-quality source knowledge. In addition, the fact that this Bayesian transfer learning (BTL) scheme does not depend on a model of interaction between the source and target tasks ensures robustness to the misspecification of such a model. The latter is a problem that affects conventional transfer learning methods. The properties of the proposed BTL scheme are demonstrated via extensive simulations, and in comparison with two contemporary alternatives.
Permanent Link: http://hdl.handle.net/11104/0326889