scholarly journals Controlled Discrete-Time Semi-Markov Random Evolutions and Their Applications

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 158
Author(s):  
Anatoliy Swishchuk ◽  
Nikolaos Limnios
Keyword(s):  
Dynamical Systems ◽  
Discrete Time ◽  
Diffusion Approximation ◽  
Limit Theorems ◽  
Stochastic Systems ◽  
Renewal Processes ◽  
Additive Functionals ◽  
Markov Random ◽  
Markov Renewal Processes ◽  
Random Evolutions

In this paper, we introduced controlled discrete-time semi-Markov random evolutions. These processes are random evolutions of discrete-time semi-Markov processes where we consider a control. applied to the values of random evolution. The main results concern time-rescaled weak convergence limit theorems in a Banach space of the above stochastic systems as averaging and diffusion approximation. The applications are given to the controlled additive functionals, controlled geometric Markov renewal processes, and controlled dynamical systems. We provide dynamical principles for discrete-time dynamical systems such as controlled additive functionals and controlled geometric Markov renewal processes. We also produce dynamic programming equations (Hamilton–Jacobi–Bellman equations) for the limiting processes in diffusion approximation such as controlled additive functionals, controlled geometric Markov renewal processes and controlled dynamical systems. As an example, we consider the solution of portfolio optimization problem by Merton for the limiting controlled geometric Markov renewal processes in diffusion approximation scheme. The rates of convergence in the limit theorems are also presented.

scholarly journals Discrete-Time Semi-Markov Random Evolutions in Asymptotic Reduced Random Media with Applications

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 963
Author(s):  
Nikolaos Limnios ◽  
Anatoliy Swishchuk
Keyword(s):  
Discrete Time ◽  
Diffusion Approximation ◽  
Random Media ◽  
Asymptotic Approximations ◽  
Asymptotic Results ◽  
Additive Functionals ◽  
Markov Random ◽  
Random Evolutions ◽  
Convergence Method ◽  
Weak Convergence Method

This paper deals with discrete-time semi-Markov random evolutions (DTSMRE) in reduced random media. The reduction can be done for ergodic and non ergodic media. Asymptotic approximations of random evolutions living in reducible random media (random environment) are obtained. Namely, averaging, diffusion approximation and normal deviation or diffusion approximation with equilibrium by martingale weak convergence method are obtained. Applications of the above results to the additive functionals and dynamical systems in discrete-time produce the above tree types of asymptotic results.

scholarly journals Discrete-Time Semi-Markov Random Evolutions and their Applications

2013 ◽  
Vol 45 (1) ◽  
pp. 214-240 ◽  
Author(s):  
Nikolaos Limnios ◽  
Anatoliy Swishchuk
Keyword(s):  
Discrete Time ◽  
Diffusion Approximation ◽  
Asymptotic Properties ◽  
Rates Of Convergence ◽  
Bellman Equations ◽  
Additive Functionals ◽  
Markov Random ◽  
Random Evolutions ◽  
Convergence Method ◽  
Hamilton Jacobi Bellman

In this paper we introduce discrete-time semi-Markov random evolutions (DTSMREs) and study asymptotic properties, namely, averaging, diffusion approximation, and diffusion approximation with equilibrium by the martingale weak convergence method. The controlled DTSMREs are introduced and Hamilton–Jacobi–Bellman equations are derived for them. The applications here concern the additive functionals (AFs), geometric Markov renewal chains (GMRCs), and dynamical systems (DSs) in discrete time. The rates of convergence in the limit theorems for DTSMREs and AFs, GMRCs, and DSs are also presented.

scholarly journals Discrete-Time Semi-Markov Random Evolutions and their Applications

2013 ◽  
Vol 45 (01) ◽  
pp. 214-240 ◽  
Author(s):  
Nikolaos Limnios ◽  
Anatoliy Swishchuk
Keyword(s):  
Discrete Time ◽  
Diffusion Approximation ◽  
Asymptotic Properties ◽  
Rates Of Convergence ◽  
Bellman Equations ◽  
Additive Functionals ◽  
Markov Random ◽  
Random Evolutions ◽  
Convergence Method ◽  
Hamilton Jacobi Bellman

In this paper we introduce discrete-time semi-Markov random evolutions (DTSMREs) and study asymptotic properties, namely, averaging, diffusion approximation, and diffusion approximation with equilibrium by the martingale weak convergence method. The controlled DTSMREs are introduced and Hamilton–Jacobi–Bellman equations are derived for them. The applications here concern the additive functionals (AFs), geometric Markov renewal chains (GMRCs), and dynamical systems (DSs) in discrete time. The rates of convergence in the limit theorems for DTSMREs and AFs, GMRCs, and DSs are also presented.

scholarly journals Limit Theorems for Markov Renewal Processes

1964 ◽  
Vol 35 (4) ◽  
pp. 1746-1764 ◽  
Author(s):  
Ronald Pyke ◽  
Ronald Schaufele
Keyword(s):  
Limit Theorems ◽  
Renewal Processes ◽  
Markov Renewal Processes

On non-singular Markov renewal processes with an application to a growth–catastrophe model

1985 ◽  
Vol 22 (02) ◽  
pp. 253-266
Author(s):  
Seppo Niemi
Keyword(s):  
Markov Process ◽  
Total Variation ◽  
Population Model ◽  
Limit Theorems ◽  
Renewal Processes ◽  
Regenerative Processes ◽  
Catastrophe Model ◽  
Markov Renewal Processes ◽  
Harris Recurrence ◽  
Forward Process

The paper is concerned with Markov renewal processes satisfying a certain non-singularity condition. The relation of this condition to irreducibility, Harris recurrence and regularity of the associated forward Markov process is studied. This enables one to prove limit theorems of a total variation type for Markov renewal processes and semi-regenerative processes by applying Orey's theorem to the forward process. The results are applied to a GI/G/1 queue and a growth-catastrophe population model.

Central limit theorems for multivariate semi-Markov sequences and processes, with applications

1999 ◽  
Vol 36 (2) ◽  
pp. 415-432 ◽  
Author(s):  
Frank Ball
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Central Limit ◽  
Limit Theorems ◽  
Markov Models ◽  
Central Limit Theorems ◽  
Renewal Processes ◽  
Counting Processes ◽  
Markov Renewal Processes ◽  
Semi Markov Processes

In this paper, central limit theorems for multivariate semi-Markov sequences and processes are obtained, both as the number of jumps of the associated Markov chain tends to infinity and, if appropriate, as the time for which the process has been running tends to infinity. The theorems are widely applicable since many functions defined on Markov or semi-Markov processes can be analysed by exploiting appropriate embedded multivariate semi-Markov sequences. An application to a problem in ion channel modelling is described in detail. Other applications, including to multivariate stationary reward processes, counting processes associated with Markov renewal processes, the interpretation of Markov chain Monte Carlo runs and statistical inference on semi-Markov models are briefly outlined.

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