An overview of Sequential Monte Carlo methods for parameter estimation in general state-space models

An overview of Sequential Monte Carlo methods for parameter estimation in general state-space models

Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, provide very good numerical approximations to the associated optimal state estimation problems. However, in many scenarios,...

Bibliográfalaš dieđut
Váldodahkkit:	Kantas, N, Doucet, A, Singh, S, MacIejowski, J
Materiálatiipa:	Journal article
Giella:	English
Almmustuhtton:	2009

Geahča maid

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Kernel sequential Monte Carlo
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Sequential Monte Carlo for model selection and estimation of neural networks
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Continuous-time non-linear non-gaussian state-space modeling of electroencephalography with sequential Monte Carlo based estimation /
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Optimal filtering for partially observed point processes using trans-dimensional sequential Monte Carlo
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Continuous-time non-linear non-gaussian state-space modeling of electroencephalography with sequential Monte Carlo based estimation [electronic resource] /
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Sequential Monte Carlo methods for demographic inference
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