  Copy number variations in cancer cells and volatility fluctuations in stock
prices are commonly manifested as changepoints occurring at the same positions
across related data sequences. We introduce a Bayesian modeling framework,
BASIC, that employs a changepoint prior to capture the co-occurrence tendency
in data of this type. We design efficient algorithms to sample from and
maximize over the BASIC changepoint posterior and develop a Monte Carlo
expectation-maximization procedure to select prior hyperparameters in an
empirical Bayes fashion. We use the resulting BASIC framework to analyze DNA
copy number variations in the NCI-60 cancer cell lines and to identify
important events that affected the price volatility of S&P 500 stocks from 2000
to 2009.
