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A Markov chain is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. It is named after the Russian mathematician Andrey Markov. Markov chains have many applications as statistical models of real-world processes, [1] [4] [5] [6] such as studying cruise control systems in motor vehicles , queues or lines of customers arriving at an airport, currency exchange rates and animal population dynamics. Markov processes are the basis for general stochastic simulation methods known as Markov chain Monte Carlo , which are used for simulating sampling from complex probability distributions, and have found application in Bayesian statistics and artificial intelligence.

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We'd like to understand how you use our websites in order to improve them. Register your interest. A general, computer-oriented method permitting to derive Markovian models with required desired properties is suggested and illustrated by examples. The method is based on the concept of a transition matrices generating tmg optimization operator, which is defined as a pair involving a linear transformation T and the associate optimization problem L T.

When the latter one is solved a set of transition matrices with required properties ergodicity, regularity etc. The optimization is performed by taking into account some constraints expressing the prior-known properties of the chain. Some fundamental properties of the resulting Markov chains are emphasized, which are useful in modeling concrete biological systems.

Thus, more realistic Markovian models are obtained starting from test data, as compared with the methods using conventional means. This is a preview of subscription content, log in to check access.

Rent this article via DeepDyve. Bharucha-Reid, A. New York: McGraw Hill Google Scholar. Capocelli, R. Chung, K. Berlin-Heidelberg-New York: Springer Cohn, H. Demetrius, L. Kybernetik 14 , — Iosifescu, M.

Tehnica IIosifescu, M. Theory, II. Acdemiei Kilmer, W. Kybernetik 16 , — Matsuyama, Y. Kybernetik 15 , — Mihoc, Gh. Academiei RSR Moran, P. Nakahama, H. Neurophysiology 29 , — Kybernetik 11 , 61—72 Kybernetik 15 , 47—64 Cybernetics 25 , — a. Tohoku J. Onicescu, O. Segundo, J. Kybernetik 3 , 67—82 Shimura, M. Kybernetik 13 , 95— Teodorescu, D. IEE , 8, — Cybernetics 24 , — Cybernetics 26 , 63—72 a. Cybernetics 28 , 83—93 b.

Facla, a. Cybernetics in print b. Tsukada, M. Cybernetics 17 , 19—28 Download references. Reprints and Permissions. Modeling Markovian biological systems via optimization operators. Cybernetics 30, — Download citation.

Received : 11 April Issue Date : September Search SpringerLink Search. Abstract A general, computer-oriented method permitting to derive Markovian models with required desired properties is suggested and illustrated by examples. Immediate online access to all issues from Subscription will auto renew annually. References Bharucha-Reid, A. Cybernetics in print b Tsukada, M. Teodorescu Authors D. Teodorescu View author publications. You can also search for this author in PubMed Google Scholar.

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