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• W2038320902 abstract "Changepoint models are widely used to model the heterogeneity of sequential data. We present a novel sequential Monte Carlo (SMC) online expectation–maximization (EM) algorithm for estimating the static parameters of such models. The SMC online EM algorithm has a cost per time which is linear in the number of particles and could be particularly important when the data is representable as a long sequence of observations, since it drastically reduces the computational requirements for implementation. We present an asymptotic analysis for the stability of the SMC estimates used in the online EM algorithm and demonstrate the performance of this scheme by using both simulated and real data originating from DNA analysis. The supplementary materials for the article are available online." @default.
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• W2038320902 date "2013-10-01" @default.
• W2038320902 modified "2023-10-18" @default.
• W2038320902 title "An Online Expectation–Maximization Algorithm for Changepoint Models" @default.
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• W2038320902 doi "https://doi.org/10.1080/10618600.2012.674653" @default.
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