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

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 predictor(s) supplied source filtering task(s) to improve its own estimate. A joint model source(s) is not required elicited. resulting decision-making for choosing optimal conditional distribution under incomplete modelling solved via fully design (FPD), i.e. appropriate minimization Kullback–Leibler divergence (KLD). FPD-optimal learner robust, in sense that it can reject poor-quality knowledge. In addition, fact this learning (BTL) scheme does depend a interaction tasks ensures robustness misspecification such model. latter affects conventional methods. properties proposed BTL are demonstrated extensive simulations, comparison with two contemporary alternatives.