Numerical determination of the distributions of stopping variables associated with sequential procedures for detecting epochs of shift in distributions of discrete random variables numerical determination of the distributions of stopping variables associated with sequential procedures

1980 ◽  
Vol 9 (1) ◽  
pp. 1-18 ◽  
Author(s):  
S Zacks
Keyword(s):  
Random Variables ◽  
Numerical Determination ◽  
Discrete Random Variables ◽  
Sequential Procedures

A storage process by semi-Markov input

1975 ◽  
Vol 7 (4) ◽  
pp. 830-844 ◽  
Author(s):  
Lajos Takács
Keyword(s):  
Recurrence Formula ◽  
Random Variables ◽  
Random Variable ◽  
Discrete Random Variable ◽  
Storage Process ◽  
Markov Sequence ◽  
Discrete Random Variables ◽  
Matrix Factorisation

A sequence of random variables η0, η1, …, ηn, … is defined by the recurrence formula ηn = max (ηn–1 + ξn, 0) where η0 is a discrete random variable taking on non-negative integers only and ξ1, ξ2, … ξn, … is a semi-Markov sequence of discrete random variables taking on integers only. Define Δ as the smallest n = 1, 2, … for which ηn = 0. The random variable ηn can be interpreted as the content of a dam at time t = n(n = 0, 1, 2, …) and Δ as the time of first emptiness. This paper deals with the determination of the distributions of ηn and Δ by using the method of matrix factorisation.

A storage process by semi-Markov input

1975 ◽  
Vol 7 (04) ◽  
pp. 830-844
Author(s):  
Lajos Takács
Keyword(s):  
Recurrence Formula ◽  
Random Variables ◽  
Random Variable ◽  
Discrete Random Variable ◽  
Storage Process ◽  
Markov Sequence ◽  
Discrete Random Variables ◽  
Matrix Factorisation

A sequence of random variables η 0, η 1, …, ηn , … is defined by the recurrence formula ηn = max (η n–1 + ξn , 0) where η 0 is a discrete random variable taking on non-negative integers only and ξ 1, ξ 2, … ξn , … is a semi-Markov sequence of discrete random variables taking on integers only. Define Δ as the smallest n = 1, 2, … for which ηn = 0. The random variable ηn can be interpreted as the content of a dam at time t = n(n = 0, 1, 2, …) and Δ as the time of first emptiness. This paper deals with the determination of the distributions of ηn and Δ by using the method of matrix factorisation.

Normal approximations to discrete unimodal distributions

1986 ◽  
Vol 23 (04) ◽  
pp. 1013-1018
Author(s):  
B. G. Quinn ◽  
H. L. MacGillivray
Keyword(s):  
Stationary Distribution ◽  
Sufficient Conditions ◽  
Random Variables ◽  
Hypergeometric Distribution ◽  
Death Process ◽  
Normal Approximations ◽  
Discrete Random Variables ◽  
Birth Death

Sufficient conditions are presented for the limiting normality of sequences of discrete random variables possessing unimodal distributions. The conditions are applied to obtain normal approximations directly for the hypergeometric distribution and the stationary distribution of a special birth-death process.

Numerical determination of surface temperature for in-depth thermocouples

1984 ◽  
Author(s):  
D. JONES ◽  
D. PATEL ◽  
E. ALEXANDER
Keyword(s):  
Surface Temperature ◽  
Numerical Determination

Experimental and Numerical Determination of Micropropulsion Device Efficiencies at Low Reynolds Numbers

2004 ◽  
Author(s):  
Andrew D. Ketsdever ◽  
Michael T. Clabough ◽  
Sergey F. Gimelshein ◽  
Alina Alexeenko
Keyword(s):  
Reynolds Numbers ◽  
Numerical Determination ◽  
Low Reynolds Numbers ◽  
Low Reynolds

scholarly journals The number of failed components in a coherent working system when the lifetimes are discretely distributed

Metrika ◽  
2021 ◽  
Author(s):  
Krzysztof Jasiński
Keyword(s):  
Random Variables ◽  
Continuous Case ◽  
Coherent System ◽  
Coherent Systems ◽  
Discrete Random Variables ◽  
Independent And Identically Distributed

AbstractIn this paper, we study the number of failed components of a coherent system. We consider the case when the component lifetimes are discrete random variables that may be dependent and non-identically distributed. Firstly, we compute the probability that there are exactly i, $$i=0,\ldots ,n-k,$$ i = 0 , … , n - k , failures in a k-out-of-n system under the condition that it is operating at time t. Next, we extend this result to other coherent systems. In addition, we show that, in the most popular model of independent and identically distributed component lifetimes, the obtained probability corresponds to the respective one derived in the continuous case and existing in the literature.

Numerical determination of three-dimensional periodic orbits generated from vertical self-resonant satellite orbits

1980 ◽  
Vol 21 (4) ◽  
pp. 395-434 ◽  
Author(s):  
I. A. Robin ◽  
V. V. Markellos
Keyword(s):  
Periodic Orbits ◽  
Three Dimensional ◽  
Satellite Orbits ◽  
Numerical Determination

scholarly journals Experimental and numerical determination of the optimum configuration of a parabolic wave extinction system for flumes

2021 ◽  
Vol 238 ◽  
pp. 109748
Author(s):  
U. Izquierdo ◽  
L. Galera-Calero ◽  
I. Albaina ◽  
A. Vázquez ◽  
G.A. Esteban ◽  
...  
Keyword(s):  
Numerical Determination ◽  
Optimum Configuration

Experimental and Numerical Determination of Roll Forming Loads

2013 ◽  
Vol 85 (1) ◽  
pp. 112-122 ◽  
Author(s):  
Peter Groche ◽  
Christian Mueller ◽  
Tilman Traub ◽  
Katja Butterweck
Keyword(s):  
Roll Forming ◽  
Numerical Determination

Two-stage stochastic programming problems involving interval discrete random variables

OPSEARCH ◽  
2012 ◽  
Vol 49 (3) ◽  
pp. 280-298 ◽  
Author(s):  
Suresh Kumar Barik ◽  
Mahendra Prasad Biswal ◽  
Debashish Chakravarty
Keyword(s):  
Stochastic Programming ◽  
Random Variables ◽  
Two Stage ◽  
Discrete Random Variables
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