Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors

Part of Advances in Neural Information Processing Systems 31 (NeurIPS 2018)

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Fei Jiang, Guosheng Yin, Francesca Dominici


Based on non-local prior distributions, we propose a Bayesian model selection (BMS) procedure for boundary detection in a sequence of data with multiple systematic mean changes. The BMS method can effectively suppress the non-boundary spike points with large instantaneous changes. We speed up the algorithm by reducing the multiple change points to a series of single change point detection problems. We establish the consistency of the estimated number and locations of the change points under various prior distributions. Extensive simulation studies are conducted to compare the BMS with existing methods, and our approach is illustrated with application to the magnetic resonance imaging guided radiation therapy data.