Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models

Kitagawa, G. (1996). Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models. Journal of Computational and Graphical Statistics, 1-25.

Type

Journal article

Intellectual base

Primary research

Year

1996

Pages

1-25

DOI

10.1080/10618600.1996.10474692

DOI registrant

Taylor & Francis

Link

https://doi.org/10.1080/10618600.1996.10474692

Cited by

1 citation across 1 artifact.

Each citation links to the sentence it supports in the citing article.

Domain-specific

• Particle Filter
  • The bootstrap filter, Monte Carlo filter, and later generalized importance-sampling formulations all preserve the same weighted-population recursion.

    This sourceDevelops sample-based prediction, filtering, and smoothing for nonlinear, non-Gaussian state-space models.

Verification

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Does it back the claim? Not recorded. The single citation of this work carries no recorded support check.

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Registry ID ref:b0388e3f1d12 · see in the full table