Sequential Monte Carlo Methods for Dynamic Systems¶
Liu, J. S., & Chen, R. (1998). Sequential Monte Carlo Methods for Dynamic Systems. Journal of the American Statistical Association, 1032-1044.
Cited by¶
1 citation across 1 artifact.
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Domain-specific¶
- Particle Filter
- Liu and Chen unified several dynamic-system Monte Carlo methods around importance sampling, resampling, rejection sampling, and Markov-chain moves; Kitagawa developed Monte Carlo filtering and smoothing for nonlinear, non-Gaussian state-space models.
This sourceUnifies dynamic-system Monte Carlo procedures through importance sampling, resampling, rejection sampling, and Markov-chain moves, with engineering and econometric examples.
- Liu and Chen unified several dynamic-system Monte Carlo methods around importance sampling, resampling, rejection sampling, and Markov-chain moves; Kitagawa developed Monte Carlo filtering and smoothing for nonlinear, non-Gaussian state-space models.
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Registry ID ref:97254b5ad9e0 · see in the full table