Expectation Propagation in Gaussian Process Dynamical Systems
File(s)
Accepted version
Author(s)
Deisenroth, Marc P
Mohamed, Shakir
Type
Conference Paper
Abstract
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 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 nonparametric probabilistic generalization of commonly used state-space models. We derive our message-passing algorithm using Expectation Propagation and 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.
Date Issued
2012-12
Citation
Advances in Neural Information Processing Systems (NIPS 2012), 2012, pp.1-9
ISBN
9781627480031
Publisher
The MIT Press
Start Page
1
End Page
9
Journal / Book Title
Advances in Neural Information Processing Systems (NIPS 2012)
Copyright Statement
© 2012 The Authors
Description
25.06.13 KB. Ok to add accepted version to Spiral, pre-proceedings and not published version.
Identifier
http://www.proceedings.com/17576.html
Source
NIPS 2012
Notes
owner: marc timestamp: 2012.06.17
Publisher URL
Start Date
2012-12-03
Finish Date
2012-12-08
Coverage Spatial
Lake Tahoe, Nevada