. . "Los procesos estoc\u00E1sticos de Gauss-Markov o cadenas de Gauss-markov (llamados as\u00ED en honor a Carl Friedrich Gauss y Andr\u00E9i M\u00E1rkov) son procesos estoc\u00E1sticos que satisfacen los requisitos para ser considerados simult\u00E1neamente procesos gaussianos y cadenas de M\u00E1rkov.\u200B\u200B Un proceso estacionario de Gauss-M\u00E1rkov es \u00FAnico[cita requerida] hasta reescalar; tal proceso es conocido tambi\u00E9n como . Cada cadena de Gauss-Markov, X(t), posee las tres propiedades siguientes:\u200B 1. \n* Si h(t) es una funci\u00F3n escalar no nula de t, entonces Z(t) = h(t)X(t) tambi\u00E9n es una cadena de Gauss-M\u00E1rkov 2. \n* Si f(t) es una funci\u00F3n escalar no decreciente de t, entonces Z(t) = X(f(t)) tambi\u00E9n es una cadena de Gauss-M\u00E1rkov 3. \n* Si el proceso es no degenerado y de cuadrado medio continuo, entonces existe una funci\u00F3n escalar no nula h(t) y una funci\u00F3n escalar estrictamente creciente f(t) tal que X(t) = h(t)W(f(t)), donde W(t) es el proceso de Wiener est\u00E1ndar. La propiedad n.\u00BA 3 nos dice que todo proceso de Gauss-M\u00E1rkov no degenerado y de cuadrado medio continuo puede ser sintetizado del proceso est\u00E1ndar de Wiener."@es . "Um processo de Gauss\u2013Markov, que recebe este nome em homenagem ao matem\u00E1tico alem\u00E3o Carl Friedrich Gauss e ao matem\u00E1tico russo Andrei Markov, \u00E9 um processo estoc\u00E1stico que satisfaz os requisitos tanto dos processos de Gauss, como dos processos de Markov. O processo de Gauss\u2013Markov estacion\u00E1rio \u00E9 tamb\u00E9m conhecido como processo de Ornstein\u2013Uhlenbeck."@pt . "Processo de Gauss\u2013Markov"@pt . . . . . "Gauss\u2013Markov 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\u2013Markov process is unique up to rescaling; such a process is also known as an Ornstein\u2013Uhlenbeck process. Gauss\u2013Markov processes obey Langevin equations."@en . . . . . . . . "Cadena de Gauss-M\u00E1rkov"@es . . . . . "1066765825"^^ . "Los procesos estoc\u00E1sticos de Gauss-Markov o cadenas de Gauss-markov (llamados as\u00ED en honor a Carl Friedrich Gauss y Andr\u00E9i M\u00E1rkov) son procesos estoc\u00E1sticos que satisfacen los requisitos para ser considerados simult\u00E1neamente procesos gaussianos y cadenas de M\u00E1rkov.\u200B\u200B Un proceso estacionario de Gauss-M\u00E1rkov es \u00FAnico[cita requerida] hasta reescalar; tal proceso es conocido tambi\u00E9n como . Cada cadena de Gauss-Markov, X(t), posee las tres propiedades siguientes:\u200B"@es . . "146285"^^ . . . . . "Um processo de Gauss\u2013Markov, que recebe este nome em homenagem ao matem\u00E1tico alem\u00E3o Carl Friedrich Gauss e ao matem\u00E1tico russo Andrei Markov, \u00E9 um processo estoc\u00E1stico que satisfaz os requisitos tanto dos processos de Gauss, como dos processos de Markov. O processo de Gauss\u2013Markov estacion\u00E1rio \u00E9 tamb\u00E9m conhecido como processo de Ornstein\u2013Uhlenbeck."@pt . . . "\u041F\u0440\u043E\u0446\u0435\u0441\u0441 \u0413\u0430\u0443\u0441\u0441\u0430 \u2014 \u041C\u0430\u0440\u043A\u043E\u0432\u0430"@ru . "Gauss\u2013Markov 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\u2013Markov process is unique up to rescaling; such a process is also known as an Ornstein\u2013Uhlenbeck process. Gauss\u2013Markov processes obey Langevin equations."@en . . "3641"^^ . . "Gauss\u2013Markov process"@en . "\u041F\u0440\u043E\u0446\u0435\u0441\u0441 \u0413\u0430\u0443\u0441\u0441\u0430 \u2014 \u041C\u0430\u0440\u043A\u043E\u0432\u0430 (\u043D\u0430\u0437\u0432\u0430\u043D \u0432 \u0447\u0435\u0441\u0442\u044C \u041A\u0430\u0440\u043B\u0430 \u0424\u0440\u0438\u0434\u0440\u0438\u0445\u0430 \u0413\u0430\u0443\u0441\u0441\u0430 \u0438 \u0410\u043D\u0434\u0440\u0435\u044F \u0410\u043D\u0434\u0440\u0435\u0435\u0432\u0438\u0447\u0430 \u041C\u0430\u0440\u043A\u043E\u0432\u0430) \u2014 \u044D\u0442\u043E \u0441\u043B\u0443\u0447\u0430\u0439\u043D\u044B\u0439 \u043F\u0440\u043E\u0446\u0435\u0441\u0441, \u043A\u043E\u0442\u043E\u0440\u044B\u0439 \u0443\u0434\u043E\u0432\u043B\u0435\u0442\u0432\u043E\u0440\u044F\u0435\u0442 \u0442\u0440\u0435\u0431\u043E\u0432\u0430\u043D\u0438\u044F\u043C \u043A\u0430\u043A \u0434\u043B\u044F \u0433\u0430\u0443\u0441\u0441\u043E\u0432\u0441\u043A\u043E\u0433\u043E \u043F\u0440\u043E\u0446\u0435\u0441\u0441\u0430, \u0442\u0430\u043A \u0438 \u0434\u043B\u044F \u043C\u0430\u0440\u043A\u043E\u0432\u0441\u043A\u043E\u0433\u043E. \u0421\u0442\u0430\u0446\u0438\u043E\u043D\u0430\u0440\u043D\u044B\u0439 \u043F\u0440\u043E\u0446\u0435\u0441\u0441 \u0413\u0430\u0443\u0441\u0441\u0430-\u041C\u0430\u0440\u043A\u043E\u0432\u0430 \u0442\u0430\u043A\u0436\u0435 \u0438\u0437\u0432\u0435\u0441\u0442\u0435\u043D \u043A\u0430\u043A ."@ru . . . . . . . . . . . . "\u041F\u0440\u043E\u0446\u0435\u0441\u0441 \u0413\u0430\u0443\u0441\u0441\u0430 \u2014 \u041C\u0430\u0440\u043A\u043E\u0432\u0430 (\u043D\u0430\u0437\u0432\u0430\u043D \u0432 \u0447\u0435\u0441\u0442\u044C \u041A\u0430\u0440\u043B\u0430 \u0424\u0440\u0438\u0434\u0440\u0438\u0445\u0430 \u0413\u0430\u0443\u0441\u0441\u0430 \u0438 \u0410\u043D\u0434\u0440\u0435\u044F \u0410\u043D\u0434\u0440\u0435\u0435\u0432\u0438\u0447\u0430 \u041C\u0430\u0440\u043A\u043E\u0432\u0430) \u2014 \u044D\u0442\u043E \u0441\u043B\u0443\u0447\u0430\u0439\u043D\u044B\u0439 \u043F\u0440\u043E\u0446\u0435\u0441\u0441, \u043A\u043E\u0442\u043E\u0440\u044B\u0439 \u0443\u0434\u043E\u0432\u043B\u0435\u0442\u0432\u043E\u0440\u044F\u0435\u0442 \u0442\u0440\u0435\u0431\u043E\u0432\u0430\u043D\u0438\u044F\u043C \u043A\u0430\u043A \u0434\u043B\u044F \u0433\u0430\u0443\u0441\u0441\u043E\u0432\u0441\u043A\u043E\u0433\u043E \u043F\u0440\u043E\u0446\u0435\u0441\u0441\u0430, \u0442\u0430\u043A \u0438 \u0434\u043B\u044F \u043C\u0430\u0440\u043A\u043E\u0432\u0441\u043A\u043E\u0433\u043E. \u0421\u0442\u0430\u0446\u0438\u043E\u043D\u0430\u0440\u043D\u044B\u0439 \u043F\u0440\u043E\u0446\u0435\u0441\u0441 \u0413\u0430\u0443\u0441\u0441\u0430-\u041C\u0430\u0440\u043A\u043E\u0432\u0430 \u0442\u0430\u043A\u0436\u0435 \u0438\u0437\u0432\u0435\u0441\u0442\u0435\u043D \u043A\u0430\u043A ."@ru . . . . . . . .