Message Passing-based Inference in Switching Autoregressive Models

Abstract

The switching autoregressive model is a flexible model for signals generated by non-stationary processes. Unfortunately, evaluation of the exact posterior distributions of the latent variables for a switching autoregressive model is analytically intractable, and this limits the applicability of switching autoregressive models in practical signal processing tasks. In this paper we present a message passing-based approach for computing approximate posterior distributions in the switching autoregressive model. Our solution tracks approximate posterior distributions in a modular way and easily extends to more complicated model variations. The proposed message passing algorithm is verified and validated on synthetic and acoustic data sets respectively.