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On‐line inference for multiple changepoint problems
Author(s) -
Fearnhead Paul,
Liu Zhen
Publication year - 2007
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/j.1467-9868.2007.00601.x
Subject(s) - resampling , particle filter , algorithm , inference , computer science , line (geometry) , filter (signal processing) , segmentation , quadratic equation , auxiliary particle filter , mathematical optimization , artificial intelligence , mathematics , computer vision , ensemble kalman filter , geometry , extended kalman filter , kalman filter
Summary. We propose an on‐line algorithm for exact filtering of multiple changepoint problems. This algorithm enables simulation from the true joint posterior distribution of the number and position of the changepoints for a class of changepoint models. The computational cost of this exact algorithm is quadratic in the number of observations. We further show how resampling ideas from particle filters can be used to reduce the computational cost to linear in the number of observations, at the expense of introducing small errors, and we propose two new, optimum resampling algorithms for this problem. One, a version of rejection control, allows the particle filter to choose the number of particles that are required at each time step automatically. The new resampling algorithms substantially outperform standard resampling algorithms on examples that we consider; and we demonstrate how the resulting particle filter is practicable for segmentation of human G+C content.