Yildirim, S and Singh, SS and Doucet, A (2012) An Online Expectation-Maximization Algorithm for Changepoint Models. Journal of Computational and Graphical Statistics.Full text not available from this repository.
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 using both simulated and real data originating from DNA analysis.
|Additional Information:||Published online on 18 Apr 2012 at http://www.tandfonline.com/action/showAxaArticles?journalCode=ucgs20#.UcrsmD6KNHk Print version Inpress|
|Divisions:||Div F > Signal Processing and Communications|
|Depositing User:||Cron Job|
|Date Deposited:||14 Mar 2012 16:10|
|Last Modified:||01 Jul 2013 01:06|
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