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

Variational Bayes for Regime-Switching Log-Normal Models

University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
Author to whom correspondence should be addressed.
Received: 14 April 2014 / Revised: 12 June 2014 / Accepted: 7 July 2014 / Published: 14 July 2014
(This article belongs to the Special Issue Information Geometry)
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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 information geometry; variational Bayes; regime-switching log-normal model; model selection; covariance estimation
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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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.

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