Back to Search
Start Over
Reduction of Markov Chains Using a Value-of-Information-Based Approach.
- Source :
-
Entropy . Apr2019, Vol. 21 Issue 4, p349-349. 1p. - Publication Year :
- 2019
-
Abstract
- In 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10994300
- Volume :
- 21
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- Entropy
- Publication Type :
- Academic Journal
- Accession number :
- 136174403
- Full Text :
- https://doi.org/10.3390/e21040349