Smoothing algorithms for state–space models

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Briers, Mark, et al. “Smoothing Algorithms for state–space Models”. Annals of the Institute of Statistical Mathematics, vol. 62, no. 1, 2009, pp. 61-89, https://doi.org/10.1007/s10463-009-0236-2.
Briers, M., Doucet, A., & Maskell, S. (2009). Smoothing algorithms for state–space models. Annals of the Institute of Statistical Mathematics, 62(1), 61-89. https://doi.org/10.1007/s10463-009-0236-2
Briers M, Doucet A, Maskell S. Smoothing algorithms for state–space models. Annals of the Institute of Statistical Mathematics. 2009;62(1):61-89.
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The category Science: Mathematics: Probabilities. Mathematical statistics 5 is the most frequently represented among the references in this article. It primarily includes studies from Journal of the American Statistical Association The chart below illustrates the number of referenced publications per year.
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The first research to cite this article was titled An Overview of Sequential Monte Carlo Methods for Parameter Estimation in General State-Space Models and was published in 2009. The most recent citation comes from a 2024 study titled An Overview of Sequential Monte Carlo Methods for Parameter Estimation in General State-Space Models . This article reached its peak citation in 2018 , with 13 citations.It has been cited in 78 different journals, 14% of which are open access. Among related journals, the IEEE Transactions on Signal Processing cited this research the most, with 16 citations. The chart below illustrates the annual citation trends for this article.
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