By Topic

Improving efficiency of implicit Markov chain state classification

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Miner, A.S. ; Dept. of Comput. Sci., Iowa State Univ., Ames, IA, USA ; Cheng, S.

Current efficient symbolic methods to classify the states of a Markov chain into transient and recurrent classes use an iterative approach, where each iteration begins by selecting a "seed" state. In this paper we present heuristics to reduce the number of iterations required. Our core contribution is the use of shortest distance information to select the seed state. Our approach uses multiway decision diagrams to represent sets of states and edge-valued decision diagrams to represent distance information. Experimental results indicate that the distance heuristics can be quite effective, often minimizing the required number of iterations.

Published in:

Quantitative Evaluation of Systems, 2004. QEST 2004. Proceedings. First International Conference on the

Date of Conference:

27-30 Sept. 2004