List of stochastic processes topics
From Wikipedia, the free encyclopedia
In the mathematics of probability, a stochastic process can be thought of as 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.
[edit] Stochastic processes topics
- This list is currently incomplete. See also Category:Stochastic_processes
- Basic affine jump diffusion
- Bernoulli process 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.
- 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
- 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)
- Girsanov's theorem
- 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
- 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
- Ornstein–Uhlenbeck process
- 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, ƒ(A) ≤ ƒ(B) with probability 1.
- Poisson process
- Population process
- Queueing theory
- Random field
- Sample-continuous process
- Stationary process
- Stochastic calculus
- Stochastic differential equation
- Stochastic process
- Telegraph process
- Time series
- Wiener process
