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Journal Article

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

Kuklišová Pavelková Lenka, Jirsa Ladislav, Quinn Anthony

: Knowledge-Based System vol.238,

: GA18-15970S, GA ČR

: Knowledge fusion, Bayesian transfer learning, Fully probabilistic design, State–space models, Bounded noise, Bayesian inference

: 10.1016/j.knosys.2021.107879

: http://library.utia.cas.cz/separaty/2022/AS/kuklisova-0551618.pdf

: https://www.sciencedirect.com/science/article/pii/S0950705121010388

(eng): 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.

: BB

: 10103