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- Gauss–Markov_process 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. The stationary Gauss–Markov process is a very special case because it is unique, except for some trivial exceptions. Every Gauss–Markov process X(t) possesses the three following properties: If h(t) is a non-zero scalar function of t, then Z(t) = h(t)X(t) is also a Gauss–Markov process If f(t) is a non-decreasing scalar function of t, then Z(t) = X(f(t)) is also a Gauss–Markov process There exists a non-zero scalar function h(t) and a non-decreasing scalar function f(t) such that X(t) = h(t)W(f(t)), where W(t) is the standard Wiener process.Property (3) means that every Gauss–Markov process can be synthesized from the standard Wiener process (SWP).".
- Gauss–Markov_process wikiPageID "146285".
- Gauss–Markov_process wikiPageRevisionID "605533260".
- Gauss–Markov_process subject Category:Markov_processes.
- Gauss–Markov_process 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. The stationary Gauss–Markov process is a very special case because it is unique, except for some trivial exceptions.".
- Gauss–Markov_process label "Gauss–Markov process".
- Gauss–Markov_process sameAs Gauss%E2%80%93Markov_process.
- Gauss–Markov_process sameAs Q5527857.
- Gauss–Markov_process sameAs Q5527857.
- Gauss–Markov_process wasDerivedFrom Gauss–Markov_process?oldid=605533260.