Abstract
Estimating the state of a nonlinear dynamic model sequentially in time is of paramount importance in applied science. Except in a few simple cases, there is no closed-form solution to this problem. It is therefore necessary to adopt numerical techniques in order to compute reasonable approximations. Sequential Monte Carlo (SMC) methods are powerful tools that allow us to accomplish this goal.
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© 2001 Springer Science+Business Media New York
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Andrieu, C., Doucet, A., Punskaya, E. (2001). Sequential Monte Carlo Methods for Optimal Filtering. In: Doucet, A., de Freitas, N., Gordon, N. (eds) Sequential Monte Carlo Methods in Practice. Statistics for Engineering and Information Science. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-3437-9_4
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DOI: https://doi.org/10.1007/978-1-4757-3437-9_4
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-2887-0
Online ISBN: 978-1-4757-3437-9
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