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In probability theory, stationary ergodic process is a stochastic process which exhibits both stationarity and ergodicity. In essence this implies that the random process will not change its statistical properties with time.## References

Stationarity is the property of a random process which guarantees that its statistical properties, such as the mean value, its moments and variance, will not change over time. A stationary process is one whose probability distribution is the same at all times. For more information see stationary process.

Several sub-types of stationarity are defined: first-order, second-order, nth-order, wide-sense and strict-sense. For details please see the reference below.

An ergodic process is one which conforms to the ergodic theorem. The theorem allows the time average of a conforming process to equal the ensemble average. In practice this means that statistical sampling can be performed at one instant across a group of identical processes or sampled over time on a single process with no change in the measured result. Please see ergodic theory.

- Peebles,P. Z., 2001, Probability, Random Variables and Random Signal Principles, McGraw-Hill Inc, Boston, ISBN 0-07-118181-4

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This article is licensed under the GNU Free Documentation License.

Last updated on Sunday June 29, 2008 at 11:48:38 PDT (GMT -0700)

View this article at Wikipedia.org - Edit this article at Wikipedia.org - Donate to the Wikimedia Foundation

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