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.
Albert Podusenko
Chief Executive Officer LazyDynamics
Albert Podusenko is a founder & CEO of Lazy Dynamics.
Bart van Erp
Chief Product Officer LazyDynamics
Bart van Erp is co-founder & product lead of Lazy Dynamics.
