Please use this identifier to cite or link to this item: https://doi.org/10.1007/s11222-011-9300-x
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dc.titleThe ensemble Kalman filter is an ABC algorithm
dc.contributor.authorNott, D.J.
dc.contributor.authorMarshall, L.
dc.contributor.authorNgoc, T.M.
dc.date.accessioned2014-10-28T05:15:49Z
dc.date.available2014-10-28T05:15:49Z
dc.date.issued2012-11
dc.identifier.citationNott, D.J., Marshall, L., Ngoc, T.M. (2012-11). The ensemble Kalman filter is an ABC algorithm. Statistics and Computing 22 (6) : 1273-1276. ScholarBank@NUS Repository. https://doi.org/10.1007/s11222-011-9300-x
dc.identifier.issn09603174
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105416
dc.description.abstractThe ensemble Kalman filter is the method of choice for many difficult high-dimensional filtering problems in meteorology, oceanography, hydrology and other fields. In this note we show that a common variant of the ensemble Kalman filter is an approximate Bayesian computation (ABC) algorithm. This is of interest for a number of reasons. First, the ensemble Kalman filter is an example of an ABC algorithm that predates the development of ABC algorithms. Second, the ensemble Kalman filter is used for very high-dimensional problems, whereas ABC methods are normally applied only in very low-dimensional problems. Third, recent state of the art extensions of the ensemble Kalman filter can also be understood within the ABC framework. © 2011 Springer Science+Business Media, LLC.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/s11222-011-9300-x
dc.sourceScopus
dc.subjectApproximate Bayesian computation
dc.subjectData assimilation
dc.subjectEnsemble Kalman filter
dc.subjectRegression adjustment
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1007/s11222-011-9300-x
dc.description.sourcetitleStatistics and Computing
dc.description.volume22
dc.description.issue6
dc.description.page1273-1276
dc.identifier.isiut000310232000009
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