The Generative Capacity of Probabilistic Splicing Systems

Authors

Mathuri Selvarajoo, Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTMJohor Bahru, Johor, MalaysiaFollow
Sherzod Turaev, Kulliyyah of Information and Communication Technology, International Islamic University Malaysia, 53100 Kuala Lumpur,MalaysiaFollow
Wan Heng Fong, Ibnu Sina Institute for Fundamental Science Studies, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, MalaysiaFollow
Nor Haniza Sarmin, Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTMJohor Bahru, Johor, MalaysiaFollow

Author Country (or Countries)

Malaysia

Abstract

The concept of probabilistic splicing system was introduced as a model for stochastic processes using DNA computing techniques. In this paper we introduce splicing systems endowed with different continuous and discrete probabilistic distributions and call them as probabilistic splicing systems. We show that any continuous distribution does not increase the generative capacity of the probabilistic splicing systems with finite components, meanwhile, some discrete distributions increase their generative capacity up to context-sensitive languages. Finally, we associate certain thresholds with probabilistic splicing systems and this increases the computational power of splicing systems with finite components.

Recommended Citation

Selvarajoo, Mathuri; Turaev, Sherzod; Heng Fong, Wan; and Haniza Sarmin, Nor (2015) "The Generative Capacity of Probabilistic Splicing Systems," Applied Mathematics & Information Sciences: Vol. 09: Iss. 3, Article 11.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol09/iss3/11