| Copyright | Copyright (c) 2009-2013, David Sorokin <david.sorokin@gmail.com> |
|---|---|
| License | BSD3 |
| Maintainer | David Sorokin <david.sorokin@gmail.com> |
| Stability | experimental |
| Safe Haskell | Safe-Inferred |
| Language | Haskell98 |
Simulation.Aivika.Parameter.Random
Description
Tested with: GHC 7.6.3
This module defines the random parameters of simulation experiments.
- randomUniform :: Double -> Double -> Parameter Double
- randomNormal :: Double -> Double -> Parameter Double
- randomExponential :: Double -> Parameter Double
- randomErlang :: Double -> Int -> Parameter Double
- randomPoisson :: Double -> Parameter Int
- randomBinomial :: Double -> Int -> Parameter Int
Documentation
Computation that generates a new random number distributed uniformly.
To create a parameter that would return the same value within the simulation run, you should memoize the computation, which is important for the Monte-Carlo simulation.
To create a random function that would return the same values in the integration
time points within the simulation run, you should either lift the computation to
the Dynamics computation and then memoize it too but using the corresponded
function for that computation, or just take the predefined function that does
namely this.
Computation that generates a new random number distributed normally.
To create a parameter that would return the same value within the simulation run, you should memoize the computation, which is important for the Monte-Carlo simulation.
To create a random function that would return the same values in the integration
time points within the simulation run, you should either lift the computation to
the Dynamics computation and then memoize it too but using the corresponded
function for that computation, or just take the predefined function that does
namely this.
Computation that returns a new exponential random number with the specified mean (the reciprocal of the rate).
To create a parameter that would return the same value within the simulation run, you should memoize the computation, which is important for the Monte-Carlo simulation.
To create a random function that would return the same values in the integration
time points within the simulation run, you should either lift the computation to
the Dynamics computation and then memoize it too but using the corresponded
function for that computation, or just take the predefined function that does
namely this.
Computation that returns a new Erlang random number with the specified scale (the reciprocal of the rate) and integer shape.
To create a parameter that would return the same value within the simulation run, you should memoize the computation, which is important for the Monte-Carlo simulation.
To create a random function that would return the same values in the integration
time points within the simulation run, you should either lift the computation to
the Dynamics computation and then memoize it too but using the corresponded
function for that computation, or just take the predefined function that does
namely this.
Computation that returns a new Poisson random number with the specified mean.
To create a parameter that would return the same value within the simulation run, you should memoize the computation, which is important for the Monte-Carlo simulation.
To create a random function that would return the same values in the integration
time points within the simulation run, you should either lift the computation to
the Dynamics computation and then memoize it too but using the corresponded
function for that computation, or just take the predefined function that does
namely this.
Computation that returns a new binomial random number with the specified probability and trials.
To create a parameter that would return the same value within the simulation run, you should memoize the computation, which is important for the Monte-Carlo simulation.
To create a random function that would return the same values in the integration
time points within the simulation run, you should either lift the computation to
the Dynamics computation and then memoize it too but using the corresponded
function for that computation, or just take the predefined function that does
namely this.