Ergodic process

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In econometrics and signal processing, a stochastic process is said to be ergodic if its statistical properties can be deduced from a single, sufficiently long, random sample of the process. The reasoning is that any collection of random samples from a process must represent the average statistical properties of the entire process. In other words, regardless of what the individual samples are, a birds-eye view of the collection of samples must represent the whole process. Conversely, a process that is not ergodic is a process that changes erratically at an inconsistent rate.[1]

Specific definitions[edit]

One can discuss the ergodicity of various statistics of a stochastic process. For example, a wide-sense stationary process has constant mean


and autocovariance


that depends only on the lag and not on time . The properties and are ensemble averages not time averages.

The process is said to be mean-ergodic[2] or mean-square ergodic in the first moment[3] if the time average estimate

converges in squared mean to the ensemble average as .

Likewise, the process is said to be autocovariance-ergodic or mean-square ergodic in the second moment[3] if the time average estimate

converges in squared mean to the ensemble average , as . A process which is ergodic in the mean and autocovariance is sometimes called ergodic in the wide sense.[3]

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

Discrete-time random processes[edit]

The notion of ergodicity also applies to discrete-time random processes for integer .

A discrete-time random process is ergodic in mean if

converges in squared mean to the ensemble average , as .

Examples of non-ergodic random processes[edit]

  • An unbiased random walk [1] is non-ergodic. Its expectation value is zero at all times, whereas its time average is a random variable with divergent variance.
  • Suppose that we have two coins: one coin is fair and the other has two heads. We choose (at random) one of the coins first, and then perform a sequence of independent tosses of our selected coin. Let X[n] denote the outcome of the nth toss, with 1 for heads and 0 for tails. Then the ensemble average is ½  (½ +  1) = ¾; yet the long-term average is ½ for the fair coin and 1 for the two-headed coin. So the long term time-average is either 1/2 or 1. Hence, this random process is not ergodic in mean.

See also[edit]


  1. ^ Originally due to L. Boltzmann. See part 2 of Vorlesungen über Gastheorie. Leipzig: J. A. Barth. 1898. OCLC 01712811. ('Ergoden' on p.89 in the 1923 reprint.) It was used to prove equipartition of energy in the kinetic theory of gases
  2. ^ Papoulis, p.428
  3. ^ 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.