# Continuous-time random walk

In mathematics, a continuous-time random walk (CTRW) is a generalization of a random walk where the wandering particle waits for a random time between jumps. It is a stochastic jump process with arbitrary distributions of jump lengths and waiting times.[1][2][3] More generally it can be seen to be a special case of a Markov renewal process.

## Motivation

CTRW was introduced by Montroll and Weiss [4] as a generalization of physical diffusion process to effectively describe anomalous diffusion, i.e., the super- and sub-diffusive cases. An equivalent formulation of the CTRW is given by generalized master equations.[5] A connection between CTRWs and diffusion equations with fractional time derivatives has been established.[6] Similarly, time-space fractional diffusion equations can be considered as CTRWs with continuously distributed jumps or continuum approximations of CTRWs on lattices. [7]

## Formulation

A simple formulation of a CTRW is to consider the stochastic process $X(t)$ defined by

$X(t) = X_0 + \sum_{i=1}^{N(t)} \Delta X_i,$

whose increments $\Delta X_i$ are iid random variables taking values in a domain $\Omega$ and $N(t)$ is the number of jumps in the interval $(0,t)$. The probability for the process taking the value $X$ at time $t$ is then given by

$P(X,t) = \sum_{n=0}^\infty P(n,t) P_n(X).$

Here $P_n(X)$ is the probability for the process taking the value $X$ after $n$ jumps, and $P(n,t)$ is the probability of having $n$ jumps after time $t$.

## Montroll-Weiss formula

We denote by $\tau$ the waiting time in between two jumps of $N(t)$ and by $\psi(\tau)$ its distribution. The Laplace transform of $\psi(\tau)$ is defined by

$\tilde{\psi}(s)=\int_0^{\infty} d\tau \, e^{-\tau s} \psi(\tau).$

Similarly, the characteristic function of the jump distribution $f(\Delta X)$ is given by its Fourier transform:

$\hat{f}(k)=\int_\Omega d(\Delta X) \, e^{i k\Delta X} f(\Delta X).$

One can show that the Laplace-Fourier transform of the probability $P(X,t)$ is given by

$\hat{\tilde{P}}(k,s) = \frac{1-\tilde{\psi}(s)}{s} \frac{1}{1-\tilde{\psi}(s)\hat{f}(k)}.$

The above is called Montroll-Weiss formula.

## Examples

The Wiener process is the standard example of a continuous time random walk in which the waiting times are exponential and the jumps are continuous and normally distributed.

## References

1. Klages, Rainer; Radons, Guenther; Sokolov, Igor M. Anomalous Transport: Foundations and Applications.
2. Paul, Wolfgang; Baschnagel, Jörg (2013-07-11). Stochastic Processes: From Physics to Finance. Springer Science & Business Media. pp. 72–. ISBN 9783319003276. Retrieved 25 July 2014.
3. Slanina, Frantisek (2013-12-05). Essentials of Econophysics Modelling. OUP Oxford. pp. 89–. ISBN 9780191009075. Retrieved 25 July 2014.
4. Elliott W. Montroll; George H. Weiss (1965). "Random Walks on Lattices. II". J. Math. Phys. 6: 167. doi:10.1063/1.1704269.
5. . M. Kenkre; E. W. Montroll; M. F. Shlesinger (1973). "Generalized master equations for continuous-time random walks". Journal of Statistical Physics. 9 (1): 45–50. doi:10.1007/BF01016796.
6. Hilfer, R.; Anton, L. (1995). "Fractional master equations and fractal time random walks". Phys. Rev. E. 51 (2): R848––R851. doi:10.1103/PhysRevE.51.R848.
7. Gorenflo, Rudolf; Mainardi, Francesco; Vivoli, Alessandro (2005). "Continuous-time random walk and parametric subordination in fractional diffusion". Chaos, Solitons \& Fractals. Elsevier. 34 (1): 87–103. doi:10.1016/j.chaos.2007.01.052.