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:
Property (3) means that every Gauss–Markov process can be synthesized from the standard Wiener process (SWP).
Properties of the Stationary GaussMarkov Processes
A stationary Gauss–Markov process with variance and time constant β ^{− 1} has the following properties.
Exponential autocorrelation:
A power spectral density (PSD) function that has the same shape as the Cauchy distribution:
(Note that the Cauchy distribution and this spectrum differ by scale factors.)
The above yields the following spectral factorization:
which is important in Wiener filtering and other areas.
There are also some trivial exceptions to all of the above.
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