Reduction of Markov Chains Using a Value-of-Information-Based Approach
AbstractIn this paper, we propose an approach to obtain reduced-order models of Markov chains. Our approach is composed of two information-theoretic processes. The first is a means of comparing pairs of stationary chains on different state spaces, which is done via the negative, modified Kullback–Leibler divergence defined on a model joint space. Model reduction is achieved by solving a value-of-information criterion with respect to this divergence. Optimizing the criterion leads to a probabilistic partitioning of the states in the high-order Markov chain. A single free parameter that emerges through the optimization process dictates both the partition uncertainty and the number of state groups. We provide a data-driven means of choosing the ‘optimal’ value of this free parameter, which sidesteps needing to a priori know the number of state groups in an arbitrary chain. View Full-Text
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Sledge, I.J.; Príncipe, J.C. Reduction of Markov Chains Using a Value-of-Information-Based Approach. Entropy 2019, 21, 349.
Sledge IJ, Príncipe JC. Reduction of Markov Chains Using a Value-of-Information-Based Approach. Entropy. 2019; 21(4):349.Chicago/Turabian Style
Sledge, Isaac J.; Príncipe, José C. 2019. "Reduction of Markov Chains Using a Value-of-Information-Based Approach." Entropy 21, no. 4: 349.
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