Ergodic behavior of a Markov chain model in a stochastic environment

被引：0

作者：

N. Tsantas

机构：

[1] Statistics and Operations Research Section,

[2] Department of Mathematics,undefined

[3] Aristotle University of Thessaloniki,undefined

[4] 54006 Thessaloniki. GREECE,undefined

[5] (e-mail: tsantas@auth.gr),undefined

来源：

Mathematical Methods of Operations Research | 2001年 / 54卷

关键词：

Key words: Markov chain models; manpower planning; limiting distribution; AMS(MOS) subject classifications. primary 60J20; secondary 90B70; 60J10;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

This paper is concerned with the asymptotic behavior of a time dependent Markov model in a stochastic environment, with special relevance to manpower systems. The stochastic concept is established through the notion of optional scenarios applied on the transition process. A theorem is provided for the existence and determination of the limiting structure of the means, variances and covariances of numbers in the classes of the system. It is also proved that, under certain conditions, the rate of convergence is geometric.

引用

页码：101 / 117

页数：16

共 50 条

[1] Ergodic behavior of a Markov chain model in a stochastic environment
Tsantas, N
[J]. MATHEMATICAL METHODS OF OPERATIONS RESEARCH, 2001, 54 (01) : 101 - 117
[2] Ergodic Stationary Distribution and Threshold Dynamics of a Stochastic Nonautonomous SIAM Epidemic Model with Media Coverage and Markov Chain
Liu, Chao
Chen, Peng
Cheung, Lora
[J]. FRACTAL AND FRACTIONAL, 2022, 6 (12)
[3] Approximation of invariant measure for a stochastic population model with Markov chain and diffusion in a polluted environment
Kang, Ting
Du, Yanyan
Ye, Ming
Zhang, Qimin
[J]. MATHEMATICAL BIOSCIENCES AND ENGINEERING, 2020, 17 (06) : 6702 - 6719
[4] ERGODIC POTENTIAL OF A MARKOV-CHAIN
SYSKI, R
[J]. ADVANCES IN APPLIED PROBABILITY, 1977, 9 (02) : 207 - 208
[5] Monte Carlo Tennis: A Stochastic Markov Chain Model
Newton, Paul K.
Aslam, Kamran
[J]. JOURNAL OF QUANTITATIVE ANALYSIS IN SPORTS, 2009, 5 (03)
[6] An Absorbing Markov Chain Model for Stochastic Memristive Devices
Malik, Adil
Papavassiliou, Christos
Stathopoulos, Spyros
[J]. 2022 11TH INTERNATIONAL CONFERENCE ON MODERN CIRCUITS AND SYSTEMS TECHNOLOGIES (MOCAST), 2022,
[7] EVOLUTION OF PROTEINS - AN ERGODIC MARKOV-CHAIN
CULLMANN, G
LABOUYGUES, JM
[J]. ORIGINS OF LIFE AND EVOLUTION OF THE BIOSPHERE, 1986, 16 (3-4): : 490 - 490
[8] Simulating wind speed data by stochastic Markov chain model
Shamshad, A
Hussin, WMAW
Bawadi, MA
Mohd, SSA
[J]. ENERGY AND ENVIRONMENT, VOLS 1 AND 2, 2003, : 326 - 332
[9] Analysis of refueling behavior of hydrogen fuel vehicles through a stochastic model using Markov Chain Process
Isaac, N.
Saha, A. K.
[J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2021, 141
[10] STRONGLY ERGODIC BEHAVIOR FOR NONSTATIONARY MARKOV PROCESSES
MADSEN, RW
ISAACSON, DL
[J]. ANNALS OF PROBABILITY, 1973, 1 (02): : 329 - 335

← 1 2 3 4 5 →