All Dates/Times are Australian Eastern Standard Time (AEST)

Technical Program

Paper Detail

Paper IDD3-S6-T3.1
Paper Title The Switching Hierarchical Gaussian Filter
Authors Ismail Senoz, Semih Akbayrak, Albert Podusenko, PhD candidate/ Technical University of Eindhoven, Netherlands; Christoph Mathys, Aarhus University, Denmark; Bert de Vries, Eindhoven University of Technology, Netherlands
Session D3-S6-T3: Message Passing
Chaired Session: Wednesday, 14 July, 23:40 - 00:00
Engagement Session: Thursday, 15 July, 00:00 - 00:20
Abstract In this paper we discuss variational message passing-based (VMP) inference in a switching Hierarchical GaussianFilter (HGF). An HGF is a flexible hierarchical state space model that supports closed-form VMP-based approximate inference for tracking of both states and slowly time-varying parameters.Since natural signals often submit to regime-switching dynamics, there is a need for low-complexity closed-form inference in switching state space models. Here we extend the HGF model with parameter switching mechanics and derive closed-formVMP update rules for plug-in applications in factor graph-based models. These VMP rules support both tracking of latent variables and variational free energy as a model performance measure. We show that the switching HGF performs better than a non-switching HGF on modelling of a stock market data set.