List of stochastic processes topics

In the mathematics of probability, a stochastic process is a random function. In practical applications, the domain over which the function is defined is a time interval (time series) or a region of space (random field).

Familiar examples of time series include stock market and exchange rate fluctuations, signals such as speech, audio and video; medical data such as a patient's EKG, EEG, blood pressure or temperature; and random movement such as Brownian motion or random walks.

Examples of random fields include static images, random topographies (landscapes), or composition variations of an inhomogeneous material.

Stochastic processes topics

 * This list is currently incomplete. See also Category:Stochastic processes


 * Basic affine jump diffusion
 * Bernoulli process: discrete-time processes with two possible states.
 * Bernoulli schemes: discrete-time processes with N possible states; every stationary process in N outcomes is a Bernoulli scheme, and vice versa.
 * Bessel process
 * Birth–death process
 * Branching process
 * Branching random walk
 * Brownian bridge
 * Brownian motion
 * Chinese restaurant process
 * CIR process
 * Continuous stochastic process
 * Cox process
 * Dirichlet processes
 * Finite-dimensional distribution
 * First passage time
 * Galton–Watson process
 * Gamma process
 * Gaussian process  – a process where all linear combinations of coordinates are normally distributed random variables.
 * Gauss–Markov process  (cf. below)
 * GenI process
 * Girsanov's theorem
 * Hawkes process
 * Homogeneous processes: processes where the domain has some symmetry and the finite-dimensional probability distributions also have that symmetry. Special cases include stationary processes, also called time-homogeneous.
 * Karhunen–Loève theorem
 * Lévy process
 * Local time (mathematics)
 * Loop-erased random walk
 * Markov processes are those in which the future is conditionally independent of the past given the present.
 * Markov chain
 * Markov chain central limit theorem
 * Continuous-time Markov process
 * Markov process
 * Semi-Markov process
 * Gauss–Markov processes: processes that are both Gaussian and Markov
 * Martingales – processes with constraints on the expectation
 * Onsager–Machlup function
 * Ornstein–Uhlenbeck process
 * Percolation theory
 * Point processes: 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, &fnof;(A) ≤ &fnof;(B) with probability 1.
 * Poisson process
 * Compound Poisson process
 * Population process
 * Probabilistic cellular automaton
 * Queueing theory
 * Queue
 * Random field
 * Gaussian random field
 * Markov random field
 * Sample-continuous process
 * Stationary process
 * Stochastic calculus
 * Itô calculus
 * Malliavin calculus
 * Semimartingale
 * Stratonovich integral
 * Stochastic control
 * Stochastic differential equation
 * Stochastic process
 * Telegraph process
 * Time series
 * Wald's martingale
 * Wiener process