Publication Details

SELECT * FROM publications WHERE Record_Number=10583
Reference TypeConference Proceedings
Author(s)Deisenroth, M.P.; Mohamed, S.
TitleExpectation Propagation in Gaussian Process Dynamical Systems
Journal/Conference/Book TitleAdvances in Neural Information Processing Systems 26 (NIPS), Cambridge, MA: MIT Press.
AbstractRich 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 as a rich model class that is appropriate for such an analysis. We present a new approximate message-passing algorithm for Bayesian state estimation and inference in Gaussian process dynamical systems, a non-parametric probabilistic generalization of commonly used state-space models. We derive our message-passing algorithm using Expectation Propagation provide a unifying perspective on message passing in general state-space models. We show that existing Gaussian filters and smoothers appear as special cases within our inference framework, and that these existing approaches can be improved upon using iterated message passing. Using both synthetic and real-world data, we demonstrate that iterated message passing can improve inference in a wide range of tasks in Bayesian state estimation, thus leading to improved predictions and more effective decision making.
PublisherThe MIT Press
Link to PDF


zum Seitenanfang