Naïve Bayes switching linear dynamical system: A model for dynamic system modelling, classification, and information fusion

The Naïve Bayes Switching Linear Dynamical System (NB-SLDS) is proposed as a novel variant of the switching linear dynamical system (SLDS). The variant models multi-variable systems that undergo regime changes in their dynamics. The model may be applied to identify regime changes or classify systems according to their dynamics. The NB-SLDS provides the means to fuse multiple sequential data sources into a single model. A key feature of the model is that it is able to handle missing and unsynchronised data. Filtering and smoothing algorithms for inference and an expectation maximisation algorithm for parameter learning in the NB-SLDS are presented. The model is demonstrated and compared to the SLDS and hidden Markov model (HMM) in a human action recognition problem.