Sequential Importance Sampling for Online Bayesian Changepoint Detection

Mavrogonatou, L. and Vyshemirsky, V. (2016) Sequential Importance Sampling for Online Bayesian Changepoint Detection. In: 22nd International Conference on Computational Statistics (COMPSTAT 2016), Oviedo, Spain, 23-26 Aug 2016, pp. 73-84. ISBN 9789073592360

Abstract

Online detection of abrupt changes in the parameters of a generative model for a time series is useful when modelling data in areas of application such as finance, robotics, and biometrics. We present an algorithm based on Sequential Importance Sampling which allows this problem to be solved in an online setting without relying on conjugate priors. Our results are exact and unbiased as we avoid using posterior approximations, and only rely on Monte Carlo integration when computing predictive probabilities. We apply the proposed algorithm to three example data sets. In two of the examples we compare our results to previously published analyses which used conjugate priors. In the third example we demonstrate an application where conjugate priors are not available. Avoiding conjugate priors allows a wider range of models to be considered with Bayesian changepoint detection, and additionally allows the use of arbitrary informative priors to quantify the uncertainty more flexibly.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Vyshemirsky, Dr Vladislav
• Authors: Mavrogonatou, L., and Vyshemirsky, V.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• ISBN: 9789073592360
• Copyright Holders: Copyright © 2016 The International Statistical Institute/International Association for Statistical Computing
• First Published: First published in 22nd International Conference on Computational Statistics (COMPSTAT 2016): 73-84
• Publisher Policy: Reproduced with the permission of the Publisher

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