The Variational Bayes Approximation In Bayesian Filtering

Abstract

The Variational Bayes (VB) approximation is applied in the context of Bayesian filtering, yielding a tractable on-line scheme for a wide range of non-stationary parametric models. This VB-filtering scheme is used to identify a Hidden Markov Model with an unknown non-stationary transition matrix. In a simulation study involving soft-bit data, reliable inference of the underlying binary sequence is achieved in tandem with estimation of the transition probabilities. The performance compares favourably with a proposed particle filtering approach, and at lower computational cost.