Online Message Passing-based Inference in the Hierarchical Gaussian Filter

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.