Entropy 2014, 16(7), 3832-3847; doi:10.3390/e16073832

Variational Bayes for Regime-Switching Log-Normal Models

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Received: 14 April 2014; in revised form: 12 June 2014 / Accepted: 7 July 2014 / Published: 14 July 2014
(This article belongs to the Special Issue Information Geometry)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: The power of projection using divergence functions is a major theme in information geometry. One version of this is the variational Bayes (VB) method. This paper looks at VB in the context of other projection-based methods in information geometry. It also describes how to apply VB to the regime-switching log-normal model and how it provides a computationally fast solution to quantify the uncertainty in the model specification. The results show that the method can recover exactly the model structure, gives the reasonable point estimates and is very computationally efficient. The potential problems of the method in quantifying the parameter uncertainty are discussed.
Keywords: information geometry; variational Bayes; regime-switching log-normal model; model selection; covariance estimation
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MDPI and ACS Style

Zhao, H.; Marriott, P. Variational Bayes for Regime-Switching Log-Normal Models. Entropy 2014, 16, 3832-3847.

AMA Style

Zhao H, Marriott P. Variational Bayes for Regime-Switching Log-Normal Models. Entropy. 2014; 16(7):3832-3847.

Chicago/Turabian Style

Zhao, Hui; Marriott, Paul. 2014. "Variational Bayes for Regime-Switching Log-Normal Models." Entropy 16, no. 7: 3832-3847.

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