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SliceDistribution—Wolfram Language Documentation

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• See Also
  • MarginalDistribution
  • Distributed
  • Probability
  • Expectation
  • StationaryDistribution
  • RandomFunction
  • RandomVariate
• Related Guides
  • Random Processes
  • Stochastic Differential Equation Processes
  • Derived Statistical Distributions
  • Probability & Statistics

SliceDistribution[proc,t]

represents the distribution of the process state at time t.

SliceDistribution[proc,{t₁,…,t_k}]

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

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

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:

Slice distribution behaves like a distribution:

Probability density function:

Characteristic function:

Moments:

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:

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:

For some continuous-time random processes, simulation of a slice distribution is not well defined:

The process path simulation between the origin and the end time depends on the choice of step:

The slice distribution simulations for a few step choices show the approximations of the exact slice distribution:

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