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,...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
2009
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