message-passing

We propose a variational message passing inference algorithm for online system identification using polynomial NARMAX models. We show empirically that our variational Bayesian estimator outperforms an online recursive least-squares estimator, most notably in small sample size settings and low noise regimes, and performs on par with an iterative least-squares estimator trained offline.

Message passing on factor graphs typically relies on a scheduling procedure, in which a central algorithm or compiler figures out which nodes should pass messages where at what time. This is not a biologically plausible mechanism. I explore the possibility of passing messages without a scheduler, where the nodes merely “react” to incoming messages.