Ergodic process

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In econometrics and signal processing, a stochastic process is said to be ergodic if its statistical properties (such as its mean and variance) can be deduced from a single, sufficiently long sample (realization) of the process.

Specific definitions[edit]

One can discuss the ergodicity of various properties of a stochastic process. For example, a wide-sense stationary process x(t) has mean m_x(t)= E[x(t)] and autocovariance r_x(\tau) = E[(x(t)-m_x(t)) (x(t+\tau)-m_x(t+\tau))] which do not change with time. One way to estimate the mean is to perform a time average:

\hat{m}_x(t)_{T} = \frac{1}{2T} \int_{-T}^{T} x(t) \, dt.

If \hat{m}_x(t)_{T} converges in squared mean to m_x(t) as T \rightarrow \infty, then the process x(t) is said to be mean-ergodic[1] or mean-square ergodic in the first moment.[2]

Likewise, one can estimate the autocovariance r_x(\tau) by performing a time average:

\hat{r}_x(\tau) = \frac{1}{2T} \int_{-T}^{T} [x(t+\tau)-m_x(t+\tau)] [x(t)-m_x(t)] \, dt.

If this expression converges in squared mean to the true autocovariance r_x(\tau) = E[(x(t+\tau)-m_x(t+\tau)) (x(t)-m_x(t))], then the process is said to be autocovariance-ergodic or mean-square ergodic in the second moment.[2]

A process which is ergodic in the first and second moments is sometimes called ergodic in the wide sense.[2]

An important example of an ergodic processes is the stationary Gaussian process with continuous spectrum.

See also[edit]


  1. ^ Papoulis, p.428
  2. ^ a b c Porat, p.14


  • Porat, B. (1994). Digital Processing of Random Signals: Theory & Methods. Prentice Hall. p. 14. ISBN 0-13-063751-3. 
  • Papoulis, Athanasios (1991). Probability, random variables, and stochastic processes. New York: McGraw-Hill. pp. 427–442. ISBN 0-07-048477-5.