On the two-filter approximations of marginal smoothing distributions in general state-space models
2018 ◽
Vol 50
(01)
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pp. 154-177
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Keyword(s):
The Past
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AbstractA prevalent problem in general state-space models is the approximation of the smoothing distribution of a state conditional on the observations from the past, the present, and the future. The aim of this paper is to provide a rigorous analysis of such approximations of smoothed distributions provided by the two-filter algorithms. We extend the results available for the approximation of smoothing distributions to these two-filter approaches which combine a forward filter approximating the filtering distributions with a backward information filter approximating a quantity proportional to the posterior distribution of the state, given future observations.
1976 ◽
Vol 8
(04)
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pp. 737-771
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Keyword(s):
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1998 ◽
Vol 7
(2)
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pp. 175
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Keyword(s):
1996 ◽
Vol 58
(3)
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pp. 597-606
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