Gauss–Markov stochastic processes (named after Carl Friedrich Gauss and Andrey Markov) are stochastic processes that satisfy the requirements for both Gaussian processes and Markov processes.
[1][2] A stationary Gauss–Markov process is unique[citation needed] up to rescaling; such a process is also known as an Ornstein–Uhlenbeck process.
Gauss–Markov processes obey Langevin equations.
[3] Every Gauss–Markov process X(t) possesses the three following properties:[4] Property (3) means that every non-degenerate mean-square continuous Gauss–Markov process can be synthesized from the standard Wiener process (SWP).
A stationary Gauss–Markov process with variance