- List of stochastic processes topics
In the

mathematics ofprobability , acan be thought of as a random function. In practical applications, the domain over which the function is defined is a time interval ("stochastic process time series ") or a region of space ("random field ").Familiar examples of

**time series**includestock market and exchange rate fluctuations, signals such as speech, audio and video; medical data such as a patient'sEKG , EEG, blood pressure or temperature; and random movement such asBrownian motion orrandom walk s.Examples of

**random fields**include static images, random topographies (landscapes), or composition variations of an inhomogeneous material.**tochastic processes topics**:"This list is currently incomplete." See also

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Bernoulli process :discrete-time processes with two possible states.

**Bernoulli scheme s: discrete-time processes with "N" possible states; every stationary process in "N" outcomes is a Bernoulli scheme, and vice-versa.

*Birth-death process

*Branching process

*Brownian bridge

*Brownian motion

*Chinese restaurant process

*CIR process

*Continuous stochastic process

*Cox process

*Dirichlet process es

*Finite-dimensional distribution

*Galton–Watson process

*Gamma process

*Gaussian process - processes where all linear combinations of coordinates are normally distributed random variables.

**Gauss-Markov process (cf. below)

*Girsanov's theorem

*Homogeneous process es: processes where the domain has somesymmetry and the finite-dimensional probability distributions also have that symmetry. Special cases includestationary process es, also called time-homogeneous.

*Karhunen-Loève theorem

*Lévy process

*Local time (mathematics)

*Loop-erased random walk

*Markov process es are those in which the future is conditionally independent of the past given the present.

**Markov chain

**Continuous-time Markov process

**Markov process

**Semi-Markov process

**Gauss-Markov process es: processes that are both Gaussian and Markov

*Martingales -- processes with constraints on the expectation

*Ornstein-Uhlenbeck process

*Point process es: random arrangements of points in a space $S$. They can be modelled as stochastic processes where the domain is a sufficiently large family of subsets of $S$, ordered by inclusion; the range is the set of natural numbers; and, if A is a subset of B, $f(A)\; le\; f(B)$ with probability 1.

*Poisson process

**Compound Poisson process

*Population process

*Queueing theory

** Queue

*Random field

**Gaussian random field

**Markov random field

*Sample continuous process

*Stationary process

*Stochastic calculus

**Itō calculus

**Malliavin calculus

**Semimartingale

**Stratonovich integral

*Stochastic differential equation

*Stochastic process

*Telegraph process

*Time series

*Wiener process

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