represents the stationary distribution of the process proc, when it exists.


  • Stationary distribution is also known as limiting distribution, steady-state distribution, and invariant distribution.
  • The stationary distribution, if it exists, is a slice distribution that is independent of the time and characterizes the limiting behavior of the process proc after all possible transients have vanished.
  • StationaryDistribution[proc] is equivalent to SliceDistribution[proc,].


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Basic Examples  (1)

Stationary distribution for an M/M/1 queue:

Probability density function:

Mean and variance:

Compute the probability of an event:

Scope  (3)

Stationary distribution may autoevaluate to known distribution:

Stationary distribution may autoevaluate to a derived distribution:

Compute the stationary distribution for a discrete Markov process:

Some slice distributions:

Stationary distribution:

Visualize the convergence to the stationary distribution using the PDF:

Properties & Relations  (3)

Stationary distribution is the SliceDistribution at infinity:

The stationary distribution may depend on the initial state:

Mean system size is the mean of the stationary distribution for a queue:

Possible Issues  (1)

The stationary distribution may exist only for a certain range of process parameters:

Introduced in 2012