2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | L.8.3 | ||
| Paper Title | Online Message Passing-based Inference in the Hierarchical Gaussian Filter | ||
| Authors | Ismail Senoz, Bert de Vries, Eindhoven University of Technology, Netherlands | ||
| Session | L.8: Learning and Message-Passing | ||
| Presentation | Lecture | ||
| Track | Statistics and Learning Theory | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| Abstract | We address the problem of online state and parameter estimation in the Hierarchical Gaussian Filter (HGF), which is a multi-layer dynamic model with non-conjugate couplings between upper-layer hidden states and parameters of a lower layer. These non-conjugacies necessitate the approximation of marginalization and expectation integrals, while the online inference constraint renders batch learning and Monte Carlo sampling unsuitable. Here we formulate the problem as a message passing task on a factor graph and propose an online variational message passing-based state and parameter tracking algorithm, which uses Gaussian quadrature to deal with non-conjugacies. We present improved message update rules for all non-conjugate couplings, thus allowing a plug-in inference method for alternative models with equivalent non-conjugate layer couplings. The method is validated on a recorded time series of Bitcoin prices. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
