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

• URL: http://arxiv.org/abs/2109.10596v1
• Date: Wed, 22 Sep 2021 08:49:15 GMT
• Title: Fully probabilistic design for knowledge fusion between Bayesian filters under uniform disturbances
• Authors: Lenka Kukli\v{s}ov\'a Pavelkov\'a (1), Ladislav Jirsa (1), Anthony Quinn (1 and 2) ((1) Czech Academy of Sciences, Institute of Information Theory and Automation, Czech Republic, (2) Trinity College Dublin, the University of Dublin, Ireland)
• Abstract summary: This paper considers the problem of Bayesian transfer learning-based knowledge fusion between linear state-space processes. A joint model of the target and source(s) is not required and is not elicited. The resulting FPD-optimal target learner is robust, in the sense that it can reject poor-quality source knowledge.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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