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