Gauss–Markov process
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Not to be confused with the Gauss–Markov theorem of mathematical statistics.
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]