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Multiscale Change Point Inference

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

Inference for Change-Point and Related Processes

Statistical MUltiscale Change point Estimation (SMUCE) is an inference tool for estimation and confidence statements about a change-point function and its main characteristics location, size and number of jumps. SMUCE detects these features on all scales in an optimal fashion. Fast computation of SMUCE via dynamic programming is addressed and data from ion channel recordings, photo emission spectroscopy and CGH array analysis will be analyzed.

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

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