A new variational Bayesian algorithm with application to human mobility pattern modeling

Wu, Burton, McGrory, Clare A. and Pettitt, Anthony N. (2012) A new variational Bayesian algorithm with application to human mobility pattern modeling. Statistics and Computing, 22 1: 185-203. doi:10.1007/s11222-010-9217-9

Author Wu, Burton
McGrory, Clare A.
Pettitt, Anthony N.
Title A new variational Bayesian algorithm with application to human mobility pattern modeling
Journal name Statistics and Computing   Check publisher's open access policy
ISSN 0960-3174
Publication date 2012-01
Year available 2010
Sub-type Article (original research)
DOI 10.1007/s11222-010-9217-9
Volume 22
Issue 1
Start page 185
End page 203
Total pages 19
Place of publication Secaucus, NJ, U.S.A.
Publisher Springer
Collection year 2012
Language eng
Formatted abstract
A new variational Bayesian (VB) algorithm, split and eliminate VB (SEVB), for modeling data via a Gaussian mixture model (GMM) is developed. This new algorithm makes use of component splitting in a way that is more appropriate for analyzing a large number of highly heterogeneous spiky spatial patterns with weak prior information than existing VB-based approaches. SEVB is a highly computationally efficient approach to Bayesian inference and like any VB-based algorithm it can perform model selection and parameter value estimation simultaneously. A significant feature of our algorithm is that the fitted number of components is not limited by the initial proposal giving increased modeling flexibility. We introduce two types of split operation in addition to proposing a new goodness-of-fit measure for evaluating mixture models. We evaluate their usefulness through empirical studies. In addition, we illustrate the utility of our new approach in an application on modeling human mobility patterns. This application involves large volumes of highly heterogeneous spiky data; it is difficult to model this type of data well using the standard VB approach as it is too restrictive and lacking in the flexibility required. Empirical results suggest that our algorithm has also improved upon the goodness-of-fit that would have been achieved using the standard VB method, and that it is also more robust to various initialization settings.
Keyword Variational Bayes (VB)
Gaussian mixture model (GMM)
Component splitting
Human mobility pattern
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status Non-UQ
Additional Notes Published online: 1 December 2010

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
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Created: Tue, 24 Jan 2012, 14:04:53 EST by Kay Mackie on behalf of School of Mathematics & Physics