Modelling using probabilistic algorithms
Markov chains are typical tools for modelling real stochastic processes. The present paper suggest to use an equivalent model of Iterative Probabilistic Algorithms, interpreted in a finite structure. The Probabilistic Algorithms model gives the possibility of modelling subprocesses and obtaining the algorithm modelling the whole process as an (algorithmic) composition of algorithms modelling subprocesses. The typical parametres (the transition matrix of the algorithm, average number of steps,… ) can be determined without experiments and compared to results of the statistical analysis of computer simulations.
Keywordsprobabilistic algorithm Markov chain probabilistic model
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- Borowska A., “Implementation of Algorithm Determining Probabilities of Behaves Probabilistic Algorithms”, MSc thesis, Technical University of Białystok, Białystok 2002Google Scholar
- Borowska A., “Determining of Probabilities of Transitions in the Probabilistic Algorithms”, MSc thesis, University of Białystok, Bialystok 1999Google Scholar
- Borowska A., Dańko W., Karbowska-Chilińska J., “Probabilistic Algorithms as a Tool for Modelling Stochastic Processes”, Proceedings of the conference CISIM 2004, 14–16 June 2004, Ełk; Poland, vol I, pp 380–389.Google Scholar
- Feldman Y., Harell D., ”A probabilistic dynamic logic”, ACM Journal of Comp., 1982Google Scholar
- Feller W., ”An Introduction to Probability Theory”, PWN, Warsaw 1977Google Scholar
- Iosifescu M., “Finite Markov Processes and Their Applications”, John Wiley & Sons, New York, London 1988Google Scholar
- Karbowska J., “Probabilistic Iterative Algorithms with Continuous Time Parameter”, Proceedings of the conference CISIM 2003, 26–28 June, 2003, Ełk; Poland, pp 133–140.Google Scholar
- Koszelew J., “The Methods for Verification Properties of Probabilistic Programs”, Ph. D. thesis IPI PAN, Warsaw 2000.Google Scholar