Fully probabilistic design for knowledge fusion between Bayesian filters under uniform disturbances
This work addresses a domain-specific problem in Bayesian transfer learning for state estimation, offering an incremental improvement over existing methods by enhancing robustness to model misspecification.
The paper tackles the problem of knowledge fusion between Bayesian filters under uniform disturbances by proposing a fully probabilistic design approach that conditions on source predictors to improve target state estimates without requiring a joint model. The method demonstrates robustness by rejecting poor-quality source knowledge and outperforms two contemporary alternatives in simulations.
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.