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In mathematics, a random walk, sometimes known as a drunkard's walk, is a stochastic process that describes a path that consists of a succession of random steps on some mathematical space. An elementary example of a random walk is the random walk on the integer number line Z {\displaystyle \mathbb {Z} } which starts at 0, and at each step moves ...
A classic example of a random walk is known as the simple random walk, which is a stochastic process in discrete time with the integers as the state space, and is based on a Bernoulli process, where each Bernoulli variable takes either the value positive one or negative one.
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.
An unbiased random walk, in any number of dimensions, is an example of a martingale. For example, consider a 1-dimensional random walk where at each time step a move to the right or left is equally likely. A gambler's fortune (capital) is a martingale if all the betting games which the gambler plays are fair.
The Drunkard's Walk discusses the role of randomness in everyday events, and the cognitive biases that lead people to misinterpret random events and stochastic processes. The title refers to a certain type of random walk, a mathematical process in which one or more variables change value under a series of random steps.
Simplified formula for the Ornstein–Uhlenbeck process from the mural shown below. Dutch artist collective De Strakke Hand: Leonard Ornstein mural, showing Ornstein as a cofounder of the Dutch Physical Society (Netherlands Physical Society) at his desk in 1921, and illustrating twice the random walk of a drunkard with a simplified formula for the Ornstein–Uhlenbeck process.
In probability theory, a stochastic process is said to have stationary increments if its change only depends on the time span of observation, but not on the time when the observation was started. Many large families of stochastic processes have stationary increments either by definition (e.g. Lévy processes) or by construction (e.g. random walks)
Donsker's invariance principle for simple random walk on .. In probability theory, Donsker's theorem (also known as Donsker's invariance principle, or the functional central limit theorem), named after Monroe D. Donsker, is a functional extension of the central limit theorem for empirical distribution functions.