aivika-4.0: A multi-paradigm simulation library

Copyright Copyright (c) 2009-2015, David Sorokin BSD3 David Sorokin experimental None Haskell2010

Simulation.Aivika.Dynamics.Random

Description

Tested with: GHC 7.8.3

This module defines the random functions that always return the same values in the integration time points within a single simulation run. The values for another simulation run will be regenerated anew.

For example, the computations returned by these functions can be used in the equations of System Dynamics.

Also it is worth noting that the values are generated in a strong order starting from `starttime` with step `dt`. This is how the `memo0Dynamics` function actually works.

Synopsis

# Documentation

Arguments

 :: Dynamics Double minimum -> Dynamics Double maximum -> Simulation (Dynamics Double)

Computation that generates random numbers distributed uniformly and memoizes them in the integration time points.

Arguments

 :: Dynamics Int minimum -> Dynamics Int maximum -> Simulation (Dynamics Int)

Computation that generates random integer numbers distributed uniformly and memoizes them in the integration time points.

Arguments

 :: Dynamics Double mean -> Dynamics Double deviation -> Simulation (Dynamics Double)

Computation that generates random numbers distributed normally and memoizes them in the integration time points.

Arguments

 :: Dynamics Double the mean (the reciprocal of the rate) -> Simulation (Dynamics Double)

Computation that generates exponential random numbers with the specified mean (the reciprocal of the rate) and memoizes them in the integration time points.

Arguments

 :: Dynamics Double the scale (the reciprocal of the rate) -> Dynamics Int the shape -> Simulation (Dynamics Double)

Computation that generates the Erlang random numbers with the specified scale (the reciprocal of the rate) and integer shape but memoizes them in the integration time points.

Arguments

 :: Dynamics Double the mean -> Simulation (Dynamics Int)

Computation that generats the Poisson random numbers with the specified mean and memoizes them in the integration time points.

Arguments

 :: Dynamics Double the probability -> Dynamics Int the number of trials -> Simulation (Dynamics Int)

Computation that generates binomial random numbers with the specified probability and trials but memoizes them in the integration time points.