represents the distribution of the process state at time t.


represents the joint distribution of process states at times t1<<tk.


  • SliceDistribution[proc,t] can be entered as proc[t].
  • SliceDistribution[proc,{t1,,tk}] can be entered as proc[{t1,,tk}].
  • For a random process xproc, its state at time t is a random variable x[t]proc[t], and its state at times t1, , tk is a random variable {x[t1],,x[tk]}proc[{t1,,tk}].
  • SliceDistribution will simplify to known special distributions whenever possible.
  • SliceDistribution can be used with such functions as Mean, CDF, and RandomVariate, etc.


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Basic Examples  (3)

Find a univariate slice distribution of a PoissonProcess:

Find a bivariate slice distribution of a WienerProcess:

Find a multivariate slice distribution of a moving-average time series:

It does not autoevaluate but behaves like a distribution:

Scope  (3)

Slice distribution behaves like a distribution:

Probability density function:

Characteristic function:


Generate a set of pseudorandom numbers:

Slice distribution may autoevaluate to known distributions:

Slice distribution for an M/M/ queue:

Probability density function:

Cumulative distribution function:

Mean of the slice distribution:

Find the limit of the mean as t approaches :

This agrees with the mean of the corresponding StationaryDistribution:

As well as the mean system size in the steady state:

Properties & Relations  (2)

Slice distribution at infinity is StationaryDistribution:

Use implicit times for computing probabilities:

Obtain the same result using the slice distribution:

Compute an expectation using implicit time in the variable x[t]:

Obtain the same result using the slice distribution:

Introduced in 2012