About: Gauss–Markov process

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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. Gauss–Markov processes obey Langevin equations.

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rdfs:label	Cadena de Gauss-Márkov (es) Gauss–Markov process (en) Processo de Gauss–Markov (pt) Процесс Гаусса — Маркова (ru)
rdfs:comment	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. Gauss–Markov processes obey Langevin equations. (en) Процесс Гаусса — Маркова (назван в честь Карла Фридриха Гаусса и Андрея Андреевича Маркова) — это случайный процесс, который удовлетворяет требованиям как для гауссовского процесса, так и для марковского. Стационарный процесс Гаусса-Маркова также известен как . (ru) Um processo de Gauss–Markov, que recebe este nome em homenagem ao matemático alemão Carl Friedrich Gauss e ao matemático russo Andrei Markov, é um processo estocástico que satisfaz os requisitos tanto dos processos de Gauss, como dos processos de Markov. O processo de Gauss–Markov estacionário é também conhecido como processo de Ornstein–Uhlenbeck. (pt) Los procesos estocásticos de Gauss-Markov o cadenas de Gauss-markov (llamados así en honor a Carl Friedrich Gauss y Andréi Márkov) son procesos estocásticos que satisfacen los requisitos para ser considerados simultáneamente procesos gaussianos y cadenas de Márkov. Un proceso estacionario de Gauss-Márkov es único[cita requerida] hasta reescalar; tal proceso es conocido también como . Cada cadena de Gauss-Markov, X(t), posee las tres propiedades siguientes: (es)
dcterms:subject	Markov processes
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Link from a Wikipage to another Wikipage	Carl Friedrich Gauss Variance Gauss–Markov theorem Ornstein–Uhlenbeck process Gaussian process Andrey Markov Autocorrelation Cauchy distribution Time constant Langevin equation Markov processes Wiener filter Wiener process Markov process Spectral density Stochastic process
sameAs	Gauss–Markov process Gauss–Markov process Gauss–Markov process Gauss–Markov process Gauss–Markov process Gauss–Markov process Gauss–Markov process Gauss–Markov process Gauss–Markov process
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has abstract	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. Gauss–Markov processes obey Langevin equations. (en) Los procesos estocásticos de Gauss-Markov o cadenas de Gauss-markov (llamados así en honor a Carl Friedrich Gauss y Andréi Márkov) son procesos estocásticos que satisfacen los requisitos para ser considerados simultáneamente procesos gaussianos y cadenas de Márkov. Un proceso estacionario de Gauss-Márkov es único[cita requerida] hasta reescalar; tal proceso es conocido también como . Cada cadena de Gauss-Markov, X(t), posee las tres propiedades siguientes: 1. * Si h(t) es una función escalar no nula de t, entonces Z(t) = h(t)X(t) también es una cadena de Gauss-Márkov 2. * Si f(t) es una función escalar no decreciente de t, entonces Z(t) = X(f(t)) también es una cadena de Gauss-Márkov 3. * Si el proceso es no degenerado y de cuadrado medio continuo, entonces existe una función escalar no nula h(t) y una función escalar estrictamente creciente f(t) tal que X(t) = h(t)W(f(t)), donde W(t) es el proceso de Wiener estándar. La propiedad n.º 3 nos dice que todo proceso de Gauss-Márkov no degenerado y de cuadrado medio continuo puede ser sintetizado del proceso estándar de Wiener. (es) Процесс Гаусса — Маркова (назван в честь Карла Фридриха Гаусса и Андрея Андреевича Маркова) — это случайный процесс, который удовлетворяет требованиям как для гауссовского процесса, так и для марковского. Стационарный процесс Гаусса-Маркова также известен как . (ru) Um processo de Gauss–Markov, que recebe este nome em homenagem ao matemático alemão Carl Friedrich Gauss e ao matemático russo Andrei Markov, é um processo estocástico que satisfaz os requisitos tanto dos processos de Gauss, como dos processos de Markov. O processo de Gauss–Markov estacionário é também conhecido como processo de Ornstein–Uhlenbeck. (pt)
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is Link from a Wikipage to another Wikipage of	List of probability topics Ornstein–Uhlenbeck process Timeline of thermodynamics Gaussian process Andrey Markov Gauss-Markov process Gauss–Markov Pao-Lu Hsu List of things named after Carl Friedrich Gauss Catalog of articles in probability theory Markov chain List of statistics articles List of stochastic processes topics List of things named after Andrey Markov
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