Article 20 pp-Vlad Stefan Barbu, Nikolaos Limnios, November 19, 2006<br/>This article presents the reliability of discrete-time semi-Markov systems. After some basic<br/>definitions and notation, we obtain explicit forms for reliability indicators. We propose nonparametric<br/>estimators for reliabi
Semi-markov reliability model of the cold standby system
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Article 12 pp,Polish Naval University
The semi-Markov reliability model of the cold standby system with renewal is
presented in the paper. The model is some modification of the model that was considered by
Barlow & Proshan (1965), Brodi & Pogosian (1978). To describe the reliability evolution of the
system, we construct a semi-Markov process by defining the states and the renewal kernel of
that one. In our model the time to failure of the system is represented by a random variable
that denotes the first passage time from the given state to the subset of states. Appropriate
theorems from the semi-Markov processes theory allow us to calculate the reliability function
and mean time to failure. As calculating an exact reliability function of the system by using
Laplace transform is often complicated we apply a theorem which deals with perturbed semi-
Markov processes to obtain an approximate reliability function of the system.
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ΠΠ°ΡΠΈΠ½ΠΎΡΡΡΠΎΠ΅Π½ΠΈΠ΅ ΠΈ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»ΠΎΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ°;Π’Π΅ΠΎΡΠΈΡ Π½Π°Π΄Π΅ΠΆΠ½ΠΎΡΡΠΈ
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Everyone working in related fields from applied mathematicians to statisticians to actuaries and operations researchers will find this a brilliantly useful practical text. The book presents applications of semi-Markov processes in finance, insurance and reliability, using real-life problems as examp
<P>This book presents applications of semi-Markov processes in finance, insurance and reliability, using real-life problems as examples. After a presentation of the main probabilistic tools necessary for understanding of the book, the authors show how to apply semi-Markov processes in finance, start
<P>Everyone working in related fields from applied mathematicians to statisticians to actuaries and operations researchers will find this a brilliantly useful practical text. The book presents applications of semi-Markov processes in finance, insurance and reliability, using real-life problems as ex