Weak convergence theorem for the ergodic distribution of a random walk with normal distributed interference of chance
In this study, a semi-Markovian random walk process (X(t)) with a discrete interference of chance is investigated. Here, it is assumed that the [[zeta].sub.n], n = 1, 2, 3, ..., which describe the discrete interference of chance are independent and identically distributed random variables having res...
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Published in | TWMS journal of applied and engineering mathematics Vol. 5; no. 1; pp. 61 - 73 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Istanbul
Turkic World Mathematical Society
01.01.2015
Elman Hasanoglu |
Subjects | |
Online Access | Get full text |
ISSN | 2146-1147 2146-1147 |
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Abstract | In this study, a semi-Markovian random walk process (X(t)) with a discrete interference of chance is investigated. Here, it is assumed that the [[zeta].sub.n], n = 1, 2, 3, ..., which describe the discrete interference of chance are independent and identically distributed random variables having restricted normal distribution with parameters (a, [[sigma].sup.2]). Under this assumption, the ergodicity of the process X(t) is proved. Moreover, the exact forms of the ergodic distribution and characteristic function are obtained. Then, weak convergence theorem for the ergodic distribution of the process [W.sub.a](t) = X(t)/a is proved under additional condition that [sigma]/a [right arrow] 0 when a [right arrow] [infinity]. Keywords: Random walk; discrete interference of chance; normal distribution; ergodic distribution; weak convergence. AMS Subject Classification: 60G50; 60K15. |
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AbstractList | In this study, a semi-Markovian random walk process (X(t)) with a discrete interference of chance is investigated. Here, it is assumed that the (n, n =1, 2, 3, ..., which describe the discrete interference of chance are independent and identically distributed random variables having restricted normal distribution with parameters (a, (2). Under this assumption, the ergodicity of the process X(t) is proved. Moreover, the exact forms of the ergodic distribution and characteristic function are obtained. Then, weak convergence theorem for the ergodic distribution of the process Wa(t) identical with X(t)/a is proved under additional condition that (/a arrow right 0 when a arrow right infinity . In this study, a semi-Markovian random walk process (X(t)) with a discrete interference of chance is investigated. Here, it is assumed that the [[zeta].sub.n], n = 1, 2, 3, ..., which describe the discrete interference of chance are independent and identically distributed random variables having restricted normal distribution with parameters (a, [[sigma].sup.2]). Under this assumption, the ergodicity of the process X(t) is proved. Moreover, the exact forms of the ergodic distribution and characteristic function are obtained. Then, weak convergence theorem for the ergodic distribution of the process [W.sub.a](t) = X(t)/a is proved under additional condition that [sigma]/a [right arrow] 0 when a [right arrow] [infinity]. Keywords: Random walk; discrete interference of chance; normal distribution; ergodic distribution; weak convergence. AMS Subject Classification: 60G50; 60K15. In this study, a semi-Markovian random walk process (X(t)) with a discrete interference of chance is investigated. Here, it is assumed that the (n, n =1, 2, 3, ..., which describe the discrete interference of chance are independent and identically distributed random variables having restricted normal distribution with parameters (a, (2). Under this assumption, the ergodicity of the process X(t) is proved. Moreover, the exact forms of the ergodic distribution and characteristic function are obtained. Then, weak convergence theorem for the ergodic distribution of the process Wa(t) ≡ X(t)/a is proved under additional condition that (/a [arrow right] 0 when a [arrow right]∞. In this study, a semi-Markovian random walk process (X(t)) with a discrete interference of chance is investigated. Here, it is assumed that the [[zeta].sub.n], n = 1, 2, 3, ..., which describe the discrete interference of chance are independent and identically distributed random variables having restricted normal distribution with parameters (a, [[sigma].sup.2]). Under this assumption, the ergodicity of the process X(t) is proved. Moreover, the exact forms of the ergodic distribution and characteristic function are obtained. Then, weak convergence theorem for the ergodic distribution of the process [W.sub.a](t) = X(t)/a is proved under additional condition that [sigma]/a [right arrow] 0 when a [right arrow] [infinity]. |
Audience | Academic |
Author | Agakishiyev, I Khaniyev, T Hanalioglu, Z |
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Copyright | COPYRIGHT 2015 Turkic World Mathematical Society Copyright Elman Hasanoglu 2015 |
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Snippet | In this study, a semi-Markovian random walk process (X(t)) with a discrete interference of chance is investigated. Here, it is assumed that the [[zeta].sub.n],... In this study, a semi-Markovian random walk process (X(t)) with a discrete interference of chance is investigated. Here, it is assumed that the (n, n =1, 2, 3,... |
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SubjectTerms | Convergence Convergence (Mathematics) Ergodic processes Functions (mathematics) Interference Mathematical analysis Mathematical research Probability distributions Random variables Random walk Random walk theory Studies Theorems Theoretical mathematics |
Title | Weak convergence theorem for the ergodic distribution of a random walk with normal distributed interference of chance |
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