Please use this identifier to cite or link to this item: https://repositorio.insper.edu.br/handle/11224/4062
Type: Artigo Científico
Title: Sequential parameter learning and filtering in structured autoregressive state-space models
Author: Prado, Raquel
Lopes, Hedibert Freitas
Publication Date: 2013
Abstract: We present particle-based algorithms for sequential filtering and parameter learning in state-space autoregressive (AR) models with structured priors. Non-conjugate priors are specified on the AR coefficients at the system level by imposing uniform or truncated normal priors on the moduli and wavelengths of the reciprocal roots of the AR characteristic polynomial. Sequential Monte Carlo algorithms are considered and implemented for on-line filtering and parameter learning within this modeling framework. More specifically, three SMC approaches are considered and compared by applying them to data simulated from different state-space AR models. An analysis of a human electroencephalogram signal is also presented to illustrate the use of the structured state-space AR models in describing biomedical signals.
Keywords (english terms): State-space autoregressions
Structured priors
Sequential filtering and parameter learning
Language: Inglês
CNPq Area: Ciências Sociais Aplicadas
URI: https://link.springer.com/article/10.1007/s11222-011-9289-1
Copyright: O INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DOS USUÁRIOS INDIVIDUAIS VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITOR.
Appears in Collections:Coleção de Artigos Científicos

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