Classifying States of a Finite Markov Chain with Membrane Computing
In this paper we present a method to classify the states of a finite Markov chain through membrane computing. A specific P system with external output is designed for each boolean matrix associated with a finite Markov chain. The computation of the system allows us to decide the convergence of the process because it determines in the environment the classification of the states (recurrent, absorbent, and transient) as well as the periods of states. The amount of resources required in the construction is polynomial in the number of states of the Markov chain.
KeywordsMarkov Chain Equivalence Class Turing Machine Priority Relation Boolean Matrix
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- 1.Cardona, M., Colomer, M.A., Miró, J., Zaragoza, A.: A step towards DNA computation model (submitted, 2006)Google Scholar
- 3.Häggström, O.: Finite Markov Chains and Algorithmic Applications. London Mathematical Society. Cambridge University Press, Cambridge (2003)Google Scholar
- 5.Shiryayev, A.N.: Probability. In: GTM 1995. Springer, Heidelberg (1984)Google Scholar
- 6.Păun, G.: Computing with membranes. Journal of Computer and System Sciences 61(1), 108–143 (2000); Turku Center for Computer Science-TUCS Report Nr. 208 (1998)Google Scholar
- 9.ISI web page: http://esi-topics.com/erf/october2003.html