Correspondence: Comments on "Performance evaluation of UKF-based nonlinear filtering"
Automatica (Journal of IFAC)
Learning curves for Gaussian processes via numerical cubature integration
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Automatica (Journal of IFAC)
A Gaussian approximation recursive filter for nonlinear systems with correlated noises
Automatica (Journal of IFAC)
Gaussian filtering and smoothing for continuous-discrete dynamic systems
Signal Processing
DSGE Model Estimation on the Basis of Second-Order Approximation
Computational Economics
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This paper proposes a numerical-integration perspective on the Gaussian filters. A Gaussian filter is approximation of the Bayesian inference with the Gaussian posterior probability density assumption being valid. There exists a variation of Gaussian filters in the literature that derived themselves from very different backgrounds. From the numerical-integration viewpoint, various versions of Gaussian filters are only distinctive from each other in their specific treatments of approximating the multiple statistical integrations. A common base is provided for the first time to analyze and compare Gaussian filters with respect to accuracy, efficiency and stability factor. This study is expected to facilitate the selection of appropriate Gaussian filters in practice and to help design more efficient filters by employing better numerical integration methods