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 inTWMS journal of applied and engineering mathematics Vol. 5; no. 1; pp. 61 - 73
Main Authors Hanalioglu, Z, Khaniyev, T, Agakishiyev, I
Format Journal Article
LanguageEnglish
Published Istanbul Turkic World Mathematical Society 01.01.2015
Elman Hasanoglu
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ISSN2146-1147
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.
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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Elman Hasanoglu
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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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