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

arXiv: https://arxiv.org/abs/2109.10596 · HTML: https://mlsanity.com/paper/2109.10596/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 59 references that are in the map.
- First posted on arXiv 22 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [Optimal Bayesian Transfer Learning](https://mlsanity.com/paper/1801.00857/) (2018)
- [Transferring model structure in Bayesian transfer learning for Gaussian process regression](https://mlsanity.com/paper/2101.06884/) (2021)
- [Gaussian Process Decentralized Data Fusion Meets Transfer Learning in Large-Scale Distributed Cooperative Perception](https://mlsanity.com/paper/1711.06064/) (2017)
- [Monte Carlo Set-Membership Filtering for Nonlinear Dynamic Systems](https://mlsanity.com/paper/1606.05046/) (2016)
- [Multi-Sensor Data and Knowledge Fusion -- A Proposal for a Terminology Definition](https://mlsanity.com/paper/2001.04171/) (2020)

## Abstract (arXiv)

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.