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Title:

Efficient Bayesian inference for nonlinear state space models with univariate autoregressive state equation

Document type:
Zeitungsartikel
Author(s):
Kreuzer, A. and Czado, C.
Abstract:
Latent autoregressive processes are a popular choice to model time varying parameters. These models can be formulated as nonlinear state space models for which inference is not straightforward due to the high number of parameters. Therefore maximum likelihood methods are often infeasible and researchers rely on alternative techniques, such as Gibbs sampling. But conventional Gibbs samplers are often tailored to specific situations and suffer from high autocorrelation among repeated draws. We pre...     »
Dewey Decimal Classification:
510 Mathematik
Journal title:
Preprint
Year:
2019
WWW:
arXiv.org
Status:
Preprint / submitted
Submitted:
27.02.2019
TUM Institution:
Professur für Angewandt Mathematische Statistik
Format:
Text
Ingested:
27.02.2019
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