Expectation Propagation in Gaussian Process Dynamical Systems: Extended Version
This work provides a unifying approach for message passing in GPDSs, which is incremental as it builds on existing methods for time-series analysis in fields like engineering and finance.
The authors tackled the problem of inference in Gaussian process dynamical systems (GPDS) for complex time-series data by proposing an expectation propagation-based message passing algorithm, which improved predictive performance compared to state-of-the-art GPDS smoothers.
Rich and complex time-series data, such as those generated from engineering systems, financial markets, videos or neural recordings, are now a common feature of modern data analysis. Explaining the phenomena underlying these diverse data sets requires flexible and accurate models. In this paper, we promote Gaussian process dynamical systems (GPDS) as a rich model class that is appropriate for such analysis. In particular, we present a message passing algorithm for approximate inference in GPDSs based on expectation propagation. By posing inference as a general message passing problem, we iterate forward-backward smoothing. Thus, we obtain more accurate posterior distributions over latent structures, resulting in improved predictive performance compared to state-of-the-art GPDS smoothers, which are special cases of our general message passing algorithm. Hence, we provide a unifying approach within which to contextualize message passing in GPDSs.