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Copy number variations in cancer cells and volatility fluctuations in stockprices are commonly manifested as changepoints occurring at the same positionsacross related data sequences. We introduce a Bayesian modeling framework,BASIC, that employs a changepoint prior to capture the co-occurrence tendencyin data of this type. We design efficient algorithms to sample from andmaximize over the BASIC changepoint posterior and develop a Monte Carloexpectation-maximization procedure to select prior hyperparameters in anempirical Bayes fashion. We use the resulting BASIC framework to analyze DNAcopy number variations in the NCI-60 cancer cell lines and to identifyimportant events that affected the price volatility of S&P 500 stocks from 2000to 2009.

Empirical Bayesian analysis of simultaneous changepoints in multiple data sequences