# What is random walk in probability?

## What is random walk in probability?

random walk, in probability theory, a process for determining the probable location of a point subject to random motions, given the probabilities (the same at each step) of moving some distance in some direction. Random walks are an example of Markov processes, in which future behaviour is independent of past history.

## What is the variance of a random walk?

This means that E[X2] = n, so var[X] = n. [In fact, more generally, if X1,…,Xn are pairwise-independent random variables, then the variance of the sum is the sum of the variances.] Since standard-deviation is the square-root of variance, we have that for our random walk, the standard deviation σ(X) = √ n.

Are random walks independent?

The definition of a random walk uses the concept of independent random variables whose technical aspects are reviewed in Chapter 1.

### What is random walk without drift?

This is the so-called random-walk-without-drift model: it assumes that, at each point in time, the series merely takes a random step away from its last recorded position, with steps whose mean value is zero.

### Does random walk have constant mean?

It can be shown that the mean of a random walk process is constant but its variance is not. Therefore a random walk process is nonstationary, and its variance increases with t.

Is a random walk invertible?

A random walk on a graph is a very special case of a Markov chain. Unlike a general Markov chain, random walk on a graph enjoys a property called time symmetry or reversibility.

## Why random walk is non stationary?

Given the way that the random walk is constructed and the results of reviewing the autocorrelation, we know that the observations in a random walk are dependent on time. The current observation is a random step from the previous observation. Therefore we can expect a random walk to be non-stationary.

## Are random walks normally distributed?

A random walk having a step size that varies according to a normal distribution is used as a model for real-world time series data such as financial markets. The Black–Scholes formula for modeling option prices, for example, uses a Gaussian random walk as an underlying assumption.

Why is a random walk not stationary?