Gauss–Markov process

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.

Basic properties
Every Gauss–Markov process X(t) possesses 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).
 * 1) If h(t) is a non-zero scalar function of t, then Z(t) = h(t)X(t) is also a Gauss–Markov process
 * 2) If f(t) is a non-decreasing scalar function of t, then Z(t) = X(f(t)) is also a Gauss–Markov process
 * 3) If the process is non-degenerate and mean-square continuous, then there exists a non-zero scalar function h(t) and a strictly increasing scalar function f(t) such that X(t) = h(t)W(f(t)), where W(t) is the standard Wiener process.

Other properties
A stationary Gauss–Markov process with variance $$\textbf{E}(X^{2}(t)) = \sigma^{2}$$ and time constant $$\beta^{-1}$$ has the following properties. = \frac{\sqrt{2\beta}\,\sigma}{(s + \beta)} \cdot\frac{\sqrt{2\beta}\,\sigma}{(-s + \beta)}. $$ which is important in Wiener filtering and other areas.
 * Exponential autocorrelation: $$\textbf{R}_{x}(\tau) = \sigma^{2}e^{-\beta |\tau|}.$$
 * A power spectral density (PSD) function that has the same shape as the Cauchy distribution: $$ \textbf{S}_{x}(j\omega) = \frac{2\sigma^{2}\beta}{\omega^{2} + \beta^{2}}.$$ (Note that the Cauchy distribution and this spectrum differ by scale factors.)
 * The above yields the following spectral factorization:$$\textbf{S}_{x}(s) = \frac{2\sigma^{2}\beta}{-s^{2} + \beta^{2}}

There are also some trivial exceptions to all of the above.