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An algorithm to segment count data using a binomial negative model

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If you have a question about this talk, please contact Mustapha Amrani .

Inference for Change-Point and Related Processes

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.

This talk is part of the Isaac Newton Institute Seminar Series series.

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