Ming-Te Cheng

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We introduce a novel generative probabilistic model for seg-mentation problems in molecular sequence analysis. All segmentations that satisfy given minimum segment length requirements are equally likely in the model. We show how segmentation-related problems can be solved with similar efficacy as in hidden Markov models. In particular , we show how the best(More)
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