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Online Inference for Multiple Changepoint Problems.

Research output: Contribution to journalJournal article


<mark>Journal publication date</mark>2007
<mark>Journal</mark>Journal of the Royal Statistical Society: Series B (Statistical Methodology)
Number of pages17
<mark>Original language</mark>English


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 propose two new, optimum resampling algorithms for this problem. One, a version of rejection control, allows the particle filter to automatically choose the number of particles required at each time-step. The new resampling algorithms substantially out-perform standard resampling algorithms on examples we consider; and we demonstrate how the resulting particle filter is practicable for segmentation of human GC content.