Adaptive sequential Monte Carlo for multiple changepoint analysis

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Title: Adaptive sequential Monte Carlo for multiple changepoint analysis
Authors: Heard, NA
Turcotte, MJM
Item Type: Journal Article
Abstract: Process monitoring and control requires detection of structural changes in a data stream in real time. This article introduces an efficient sequential Monte Carlo algorithm designed for learning unknown changepoints in continuous time. The method is intuitively simple: new changepoints for the latest window of data are proposed by conditioning only on data observed since the most recent estimated changepoint, as these observations carry most of the information about the current state of the process. The proposed method shows improved performance over the current state of the art. Another advantage of the proposed algorithm is that it can be made adaptive, varying the number of particles according to the apparent local complexity of the target changepoint probability distribution. This saves valuable computing time when changes in the changepoint distribution are negligible, and enables re-balancing of the importance weights of existing particles when a significant change in the target distribution is encountered. The plain and adaptive versions of the method are illustrated using the canonical continuous time changepoint problem of inferring the intensity of an inhomogeneous Poisson process, although the method is generally applicable to any changepoint problem. Performance is demonstrated using both conjugate and non-conjugate Bayesian models for the intensity. Appendices to the article are available online, illustrating the method on other models and applications.
Issue Date: 21-May-2016
Date of Acceptance: 10-May-2016
URI: http://hdl.handle.net/10044/1/34958
DOI: https://dx.doi.org/10.1080/10618600.2016.1190281
ISSN: 1537-2715
Publisher: Taylor & Francis
Start Page: 414
End Page: 423
Journal / Book Title: Journal of Computational and Graphical Statistics
Volume: 26
Issue: 2
Copyright Statement: © 2016 Taylor & Francis. This is an Author's Accepted Manuscript of an article published in the Journal of Computational and Graphical Statistics (2016) available online at: http://www.tandfonline.com/10.1080/10618600.2016.1190281
Keywords: Science & Technology
Physical Sciences
Statistics & Probability
Mathematics
Adaptive sample size
Markov chain Monte Carlo methods
Online inference
Particle filters
ONLINE INFERENCE
PARTICLE FILTERS
MODELS
SAMPLERS
POINT
0104 Statistics
Statistics & Probability
Publication Status: Published
Appears in Collections:Mathematics
Statistics
Faculty of Natural Sciences



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