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While frequentist methods have yielded online filtering and\nprediction techniques, most Bayesian papers have focused on the retrospective\nsegmentation problem. Here we examine the case where the model parameters\nbefore and after the changepoint are independent and we derive an online\nalgorithm for exact inference of the most recent changepoint. We compute the\nprobability distribution of the length of the current ``run,'' or time since\nthe last changepoint, using a simple message-passing algorithm. Our\nimplementation is highly modular so that the algorithm may be applied to a\nvariety of types of data. 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