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Segment parameter labelling in MCMC mean-shift change detection

Ahrabian, Alireza, Enshaeifar, Shirin, Cheong Took, Clive and Barnaghi, Payam (2018) Segment parameter labelling in MCMC mean-shift change detection In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing, 15 - 20 April 2018, Calgary, Alberta, Canada.

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Abstract

This work addresses the problem of segmentation in time series data with respect to a statistical parameter of interest in Bayesian models. It is common to assume that the parameters are distinct within each segment. As such, many Bayesian change point detection models do not exploit the segment parameter patterns, which can improve performance. This work proposes a Bayesian mean-shift change point detection algorithm that makes use of repetition in segment parameters, by introducing segment class labels that utilise a Dirichlet process prior. The performance of the proposed approach was assessed on both synthetic and real world data, highlighting the enhanced performance when using parameter labelling.

Item Type: Conference or Workshop Item (Conference Poster)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Ahrabian, Alirezaa.ahrabian@surrey.ac.uk
Enshaeifar, Shirins.enshaeifar@surrey.ac.uk
Cheong Took, Clivec.cheongtook@surrey.ac.uk
Barnaghi, PayamP.Barnaghi@surrey.ac.uk
Date : 20 April 2018
Copyright Disclaimer : © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Uncontrolled Keywords : Mean-Shift Change Detection, Markov Chain Monte Carlo, Dirichlet Process, Nonparametric Bayesian
Related URLs :
Depositing User : Melanie Hughes
Date Deposited : 06 Feb 2018 10:28
Last Modified : 20 Apr 2018 02:08
URI: http://epubs.surrey.ac.uk/id/eprint/845751

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