The first passage time for position-dependent correlated random walk with absorbing boundary condition

2019 ◽  
Vol 2019 (7) ◽  
pp. 073201
Author(s):  
Jianliang Tang ◽  
Mingqing Xiao
Keyword(s):  
Boundary Condition ◽  
Random Walk ◽  
First Passage Time ◽  
Passage Time ◽  
Absorbing Boundary Condition ◽  
Correlated Random Walk ◽  
First Passage ◽  
Absorbing Boundary

scholarly journals Time-Inhomogeneous Feller-type Diffusion Process with Absorbing Boundary Condition

2021 ◽  
Vol 183 (3) ◽  
Author(s):  
Virginia Giorno ◽  
Amelia G. Nobile
Keyword(s):  
Diffusion Process ◽  
Noise Intensity ◽  
First Passage Time ◽  
Transition Density ◽  
Passage Time ◽  
Intensity Function ◽  
Absorbing Boundary Condition ◽  
First Passage ◽  
Absorbing Boundary ◽  
Time Density

AbstractA time-inhomogeneous Feller-type diffusion process with linear infinitesimal drift $$\alpha (t)x+\beta (t)$$ α ( t ) x + β ( t ) and linear infinitesimal variance 2r(t)x is considered. For this process, the transition density in the presence of an absorbing boundary in the zero-state and the first-passage time density through the zero-state are obtained. Special attention is dedicated to the proportional case, in which the immigration intensity function $$\beta (t)$$ β ( t ) and the noise intensity function r(t) are connected via the relation $$\beta (t)=\xi \,r(t)$$ β ( t ) = ξ r ( t ) , with $$0\le \xi <1$$ 0 ≤ ξ < 1 . Various numerical computations are performed to illustrate the effect of the parameters on the first-passage time density, by assuming that $$\alpha (t)$$ α ( t ) , $$\beta (t)$$ β ( t ) or both of these functions exhibit some kind of periodicity.

scholarly journals Tracking a random walk first-passage time through noisy observations

2012 ◽  
Vol 22 (5) ◽  
pp. 1860-1879 ◽  
Author(s):  
Marat V. Burnashev ◽  
Aslan Tchamkerten
Keyword(s):  
Random Walk ◽  
First Passage Time ◽  
Passage Time ◽  
First Passage

scholarly journals Fractional compound Poisson processes with multiple internal states

2018 ◽  
Vol 13 (1) ◽  
pp. 10 ◽  
Author(s):  
Pengbo Xu ◽  
Weihua Deng
Keyword(s):  
Stochastic Process ◽  
Random Walk ◽  
Waiting Time ◽  
Anomalous Diffusion ◽  
First Passage Time ◽  
Passage Time ◽  
Poisson Processes ◽  
First Passage ◽  
Internal States ◽  
Functional Distribution

For the particles undergoing the anomalous diffusion with different waiting time distributions for different internal states, we derive the Fokker-Planck and Feymann-Kac equations, respectively, describing positions of the particles and functional distributions of the trajectories of particles; in particular, the equations governing the functional distribution of internal states are also obtained. The dynamics of the stochastic processes are analyzed and the applications, calculating the distribution of the first passage time and the distribution of the fraction of the occupation time, of the equations are given. For the further application of the newly built models, we make very detailed discussions on the none-immediately-repeated stochastic process, e.g., the random walk of smart animals.

Random walk and first passage time on a weighted hierarchical network

2014 ◽  
Vol 25 (09) ◽  
pp. 1450037 ◽  
Author(s):  
Feng Zhu ◽  
Meifeng Dai ◽  
Yujuan Dong ◽  
Jie Liu
Keyword(s):  
Random Walk ◽  
First Passage Time ◽  
Passage Time ◽  
Scale Factor ◽  
Hierarchical Network ◽  
Self Similarity ◽  
First Passage ◽  
Hierarchical Networks ◽  
Average Value ◽  
Peripheral Node

This paper reports a weighted hierarchical network generated on the basis of self-similarity, in which each edge is assigned a different weight in the same scale. We studied two substantial properties of random walk: the first-passage time (FPT) between a hub node and a peripheral node and the FPT from a peripheral node to a local hub node over the network. Meanwhile, an analytical expression of the average sending time (AST) is deduced, which reflects the average value of FPT from a hub node to any other node. Our result shows that the AST from a hub node to any other node is related to the scale factor and the number of modules. We found that the AST grows sublinearly, linearly and superlinearly respectively with the network order, depending on the range of the scale factor. Our work may shed some light on revealing the diffusion process in hierarchical networks.

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