Time-dependent step–step correlations in a self-avoiding random walk

1981 ◽  
Vol 19 (3) ◽  
pp. 499-506 ◽  
Author(s):  
Y. Khwaja ◽  
A. Sadiq
Keyword(s):  
Random Walk ◽  
Time Dependent ◽  
Step Step

Stochastic inflation as a time-dependent random walk

1989 ◽  
Vol 39 (8) ◽  
pp. 2245-2252 ◽  
Author(s):  
Henry E. Kandrup
Keyword(s):  
Random Walk ◽  
Time Dependent

scholarly journals A Realization of a Quasi-Random Walk for Atoms in Time-Dependent Optical Potentials

Atoms ◽  
2015 ◽  
Vol 3 (3) ◽  
pp. 433-449 ◽  
Author(s):  
Torsten Hinkel ◽  
Helmut Ritsch ◽  
Claudiu Genes
Keyword(s):  
Random Walk ◽  
Time Dependent ◽  
Optical Potentials

The existence of moments for stationary Markov chains

1983 ◽  
Vol 20 (01) ◽  
pp. 191-196 ◽  
Author(s):  
R. L. Tweedie
Keyword(s):  
Markov Chain ◽  
Random Walk ◽  
Markov Chains ◽  
Stationary Distribution ◽  
Time Dependent ◽  
General Function ◽  
Convergence Of Moments ◽  
Special Cases

We give conditions under which the stationary distribution π of a Markov chain admits moments of the general form ∫ f(x)π(dx), where f is a general function; specific examples include f(x) = xr and f(x) = esx . In general the time-dependent moments of the chain then converge to the stationary moments. We show that in special cases this convergence of moments occurs at a geometric rate. The results are applied to random walk on [0, ∞).

scholarly journals Random walk in degree space and the time-dependent Watts-Strogatz model

2017 ◽  
Vol 95 (1) ◽  
Author(s):  
H. L. Casa Grande ◽  
M. Cotacallapa ◽  
M. O. Hase
Keyword(s):  
Random Walk ◽  
Time Dependent

scholarly journals Transient solution to the time-dependent multiserver Poisson queue

2005 ◽  
Vol 42 (3) ◽  
pp. 766-777 ◽  
Author(s):  
B. H. Margolius
Keyword(s):  
Random Walk ◽  
Transition Probabilities ◽  
Arrival Rate ◽  
Time Dependent ◽  
Time Varying ◽  
Expected Number ◽  
Straightforward Application ◽  
Transient Probabilities ◽  
Poisson Arrivals ◽  
The Right

We derive an integral equation for the transient probabilities and expected number in the queue for the multiserver queue with Poisson arrivals, exponential service for time-varying arrival and departure rates, and a time-varying number of servers. The method is a straightforward application of generating functions. We can express pĉ−1(t), the probability that ĉ − 1 customers are in the queue or being served, in terms of a Volterra equation of the second kind, where ĉ is the maximum number of servers working during the day. Each of the other transient probabilities is expressed in terms of integral equations in pĉ−1(t) and the transition probabilities of a certain time-dependent random walk. In this random walk, the rate of steps to the right equals the arrival rate of the queue and the rate of steps to the left equals the departure rate of the queue when all servers are busy.

scholarly journals Transient solution to the time-dependent multiserver Poisson queue

2005 ◽  
Vol 42 (03) ◽  
pp. 766-777 ◽  
Author(s):  
B. H. Margolius
Keyword(s):  
Random Walk ◽  
Transition Probabilities ◽  
Arrival Rate ◽  
Time Dependent ◽  
Time Varying ◽  
Expected Number ◽  
Straightforward Application ◽  
Transient Probabilities ◽  
Poisson Arrivals ◽  
The Right

We derive an integral equation for the transient probabilities and expected number in the queue for the multiserver queue with Poisson arrivals, exponential service for time-varying arrival and departure rates, and a time-varying number of servers. The method is a straightforward application of generating functions. We can express p ĉ−1(t), the probability that ĉ − 1 customers are in the queue or being served, in terms of a Volterra equation of the second kind, where ĉ is the maximum number of servers working during the day. Each of the other transient probabilities is expressed in terms of integral equations in p ĉ−1(t) and the transition probabilities of a certain time-dependent random walk. In this random walk, the rate of steps to the right equals the arrival rate of the queue and the rate of steps to the left equals the departure rate of the queue when all servers are busy.

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