Abstract
We consider the problem of smoothing a continuous-time random process using irregularly spaced noisy samples. If the random process to be smoothed is generated by a linear state model, the relevant Hamiltonian system can be easily derived using the concept of complementary model, introduced by Weinert and Desai [1]. All smoothing algorithms can then be obtained via various changes of variables in the Hamiltonian system.
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References
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© 1984 Springer-Verlag Berlin Heidelberg
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Weinert, H.L. (1984). The Complementary Model in Continuous/Discrete Smoothing. In: Parzen, E. (eds) Time Series Analysis of Irregularly Observed Data. Lecture Notes in Statistics, vol 25. Springer, New York, NY. https://doi.org/10.1007/978-1-4684-9403-7_17
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DOI: https://doi.org/10.1007/978-1-4684-9403-7_17
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-96040-1
Online ISBN: 978-1-4684-9403-7
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