Results 261 to 270 of about 15,837 (289)

Future pricing through homogeneous semi-Markov processes

Applied Stochastic Models in Business and Industry, 2005
A model for future currency pricing is considered based on a discrete time homogeneous semi-Markov process with finite state space. It is applied to the data on Italian lire from 27 March 98 to 17 September 98. The values of raw time series are discretized to obtain 122 states of the process.
Giuseppe Di Biase, Jacques Janssen, MANCA, Raimondo   +2 more
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Reward processes for semi-Markov processes: asymptotic behaviour

Journal of Applied Probability, 1998
The asymptotic behaviour of the cumulative mean of a reward process 𝒵ρ, where the reward function ρ belongs to a rather large class of functions, is obtained. It is proved that E𝒵ρ(t) = C0 + C1t + o(1), t → ∞, where C0 and C1 are fully specified. A section is devoted to the dual process of a semi-Markov process, and a formula is given for the mean of ...
Soltani, A. Reza, Khorshidian, K.
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Continuity of Generalized Semi-Markov Processes

Mathematics of Operations Research, 1980
It is shown that sequences of generalized semi-Markov processes converge in the sense of weak convergence of random functions if associated sequences of defining elements (initial distributions, transition functions and clock time distributions) converge.
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Average cost semi-markov decision processes

Journal of Applied Probability, 1970
The semi-Markov decision model is considered under the criterion of long-run average cost. A new criterion, which for any policy considers the limit of the expected cost incurred during the first n transitions divided by the expected length of the first n transitions, is considered.
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Discounted semi-markov decision process in a semi-markov environment

Optimization, 1997
This paper presents the discounted semi-Markov decision process (SMDP) with Borel state space in a semi-Markov environment. It describes a system which behaves like a SMDP except that the system is influenced by its semi-Markov process environment. Following each state transition of the environment, the parameters of the SMDP changes.
openaire   +1 more source

Markov and Semi-Markov Processes

2018
This chapter is devoted to jump Markov processes and finite semi-Markov processes. In both cases, the index is considered as the calender time, continuously counted over the positive real line. Markov processes are continuous-time processes that share the Markov property with the discrete-time Markov chains.
Valérie Girardin, Nikolaos Limnios
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On reversible semi-Markov processes

Operations Research Letters, 1994
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Generalized semi-Markov decision processes

Journal of Applied Probability, 1979
Various authors have derived the necessary and sufficient conditions for optimality in semi-Markov decision processes in which the state remains constant between jumps. In this paper similar results are presented for a generalized semi-Markov decision process in which the state varies between jumps according to a Markov process with continuous sample ...
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Semi-Markov failure rates processes

Applied Mathematics and Computation, 2011
Let \(\{\lambda(u):u\geq 0\}\) be a semi-Markov process defined by a Markov renewal kernel. The author studies the survival function \(R\) defined as the expectation of \(\exp \left(-\int_0^t\lambda(u)du\right)\), for \(t \geq 0\). Note that, \(R(t)\) is considered to be the reliability of an object with a random failure rate.
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Controlled semi-Markov processes

Cybernetics, 1974
L. G. Gubenko, �. S. Shtatland
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