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 . A stationary Gauss–Markov process is unique up to rescaling; such a process is also known as an Ornstein–Uhlenbeck process .
3-451: (Redirected from Gauss-Markov ) The phrase Gauss–Markov is used in two different ways: Gauss–Markov processes in probability theory The Gauss–Markov theorem in mathematical statistics (in this theorem, one does not assume the probability distributions are Gaussian.) [REDACTED] Topics referred to by the same term This disambiguation page lists mathematics articles associated with
6-572: The same title. If an internal link led you here, you may wish to change the link to point directly to the intended article. Retrieved from " https://en.wikipedia.org/w/index.php?title=Gauss–Markov&oldid=824162160 " Category : Mathematics disambiguation pages Hidden categories: Short description is different from Wikidata All article disambiguation pages All disambiguation pages Gauss%E2%80%93Markov process Gauss–Markov processes obey Langevin equations . Every Gauss–Markov process X ( t ) possesses
9-510: The three following properties: 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 E ( X 2 ( t ) ) = σ 2 {\displaystyle {\textbf {E}}(X^{2}(t))=\sigma ^{2}} and time constant β − 1 {\displaystyle \beta ^{-1}} has
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