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  • 统计与管理学院2017年学术报告第82期

    【主 题】 Bayesian Model Selection Approach to Multiple Change Points Detection with Non-Local Priors

    【报告人】 江 非 助教授

    香港大学

    【时 间】 2017年12月20日(星期三)10:30-11:30

    【地 点】 上海财经大学统计与管理学院大楼1316会议室

    摘 要】We propose a Bayesian model selection (BMS) change point detection procedure using non-local prior distributions for a sequence of data with multiple mean shift changes. By using the non-local priors in the Bayesian model selection framework, the BMS method can effectively suppress the flat points. Further, 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. From both theoretical and numerical perspectives, we show that the non-local inverse moment prior leads to the fastest convergence rate in identifying the true change points. Extensive simulation studies are conducted to compare the BMS with existing methods, and our method is illustrated with application to the magnetic resonance imaging guided radiation therapy data.