Next Article in Journal
Hierarchical Geometry Verification via Maximum Entropy Saliency in Image Retrieval
Next Article in Special Issue
The Entropy-Based Quantum Metric
Previous Article in Journal
Phase Competitions behind the Giant Magnetic Entropy Variation: Gd5Si2Ge2 and Tb5Si2Ge2 Case Studies
Previous Article in Special Issue
Extending the Extreme Physical Information to Universal Cognitive Models via a Confident Information First Principle
Entropy 2014, 16(7), 3832-3847; doi:10.3390/e16073832
Article

Variational Bayes for Regime-Switching Log-Normal Models

 and *
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)
View Full-Text   |   Download PDF [743 KB, uploaded 24 February 2015]   |   Browse Figures

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 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 which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share & Cite This Article

Export to BibTeX |
EndNote


MDPI and ACS Style

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

View more citation formats

Article Metrics

Comments

Citing Articles

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert