Conditional particle filters with diffuse initial distributions

Conditional particle filters (CPFs) are powerful smoothing algorithms for general nonlinear/non-Gaussian hidden Markov models. However, CPFs can be inefficient or difficult to apply with diffuse initial distributions, which are common in statistical applications. We propose a simple but generally ap...

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Detalles Bibliográficos
Autores principales: Karppinen, Santeri, Vihola, Matti
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7926083/
https://www.ncbi.nlm.nih.gov/pubmed/33679010
http://dx.doi.org/10.1007/s11222-020-09975-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7926083/
https://www.ncbi.nlm.nih.gov/pubmed/33679010
http://dx.doi.org/10.1007/s11222-020-09975-1

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