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An algorithm to segment count data using a binomial negative model

Date: 
Thursday 16th January 2014 - 10:00 to 10:30
Venue: 
INI Seminar Room 1
Abstract: 
We consider the problem of segmenting a count data profile. We developed an algorithm to recover the best (w.r.t the likelihood) segmentations in 1 to K_{max} segments. We prove that the optimal segmentation can be recovered using a compression scheme which reduces the time complexity. The compression is particularly efficient when the signal has large plateaus. We illustrate our algorithm on next generation sequencing data.
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Presentation Material: 
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons