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A stochastic process describes the evolution of random variables/systems over time and/or space and/or any other index set. It has applications in areas such as econometrics, weather, signal processing, etc. Examples: Gaussian process, Markov process, etc.

A stochastic process is a collection of random variables $${\bf X} = \{ X_t : t \in T \}$$ defined on a common probability space, taking values in a common set $$S$$ (state space), and indexed by set $$T$$, often either $$\mathbb{N}$$ or $$[0, \infty)$$, and thought of as time (either continuous or discrete). (Reference: Random Services)

Common examples of stochastic processes are:

• Random Walks
• Simple random walk: defined on the integers in discrete time, and is based on a Bernoulli process, where each $$iid$$ Bernoulli variable takes either the value $$+1$$ or $$-1$$.
• Bernoulli Process
• A sequence of $$iid$$ Bernoulli random variables, where each event is a Bernoulli trial.
• Wiener Process
• Stochastic process with stationary and independent increments whose size is normally distributed.
• Poisson Process
• Defined as a counting process, which is a process that represents the random number of points or events up to some time.
• The number of points of the process that are located in the interval from zero to some (given, non-random) time is a Poisson random variable.
• Markov Chains (both continuous and discrete time)
• The behavior of the process in the future is stochastically independent of its behavior in the past, given the current state of the process.
• Martingales
• A discrete-time or continuous-time stochastic processes with the property that the expectation of the next value of a martingale is equal to the current value given all the previous values of the process.

(Reference: Wikipedia)