mc_integral: (Improved) Monte Carlo integration
In david850803/MC.makes.perfect: Monte Carlo integration
Description
Usage
Arguments
Details
References
See Also
Examples
Description
Monte Carlo integral of ftn over [a, b] using a sample of size n.
Usage
1
mc_integral(ftn, a, b, n)
Arguments
a
lower bound of the integration. Must be finite.
b
upper bound of the integration. Must be finite and assume b > a.
n
number of samples used in the estimation.
Details
When we say a Monte Carlo technique is better than another we mean that using the same number of function calls, it has smaller variance.Because our estimates are based on random samples they are themselves random variables.
References
Owen Jones,Robert Maillardet,Andrew Robinson (2014).Introduction to Scientific Programming and Simulation Using R ; Second Edition
See Also
Examples
1
2
f <- function(x) return(x^3 + 1)
mc_integral(f, 0, 1, 10000)
david850803/MC.makes.perfect documentation built on June 16, 2019, 12:04 a.m.
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Description Usage Arguments Details References See Also Examples
Description
Monte Carlo integral of ftn over [a, b] using a sample of size n.
Usage
1 | mc_integral(ftn, a, b, n)
|
Arguments
a |
lower bound of the integration. Must be finite. |
b |
upper bound of the integration. Must be finite and assume b > a. |
n |
number of samples used in the estimation. |
Details
When we say a Monte Carlo technique is better than another we mean that using the same number of function calls, it has smaller variance.Because our estimates are based on random samples they are themselves random variables.
References
Owen Jones,Robert Maillardet,Andrew Robinson (2014).Introduction to Scientific Programming and Simulation Using R ; Second Edition
See Also
Examples
1 2 | f <- function(x) return(x^3 + 1)
mc_integral(f, 0, 1, 10000)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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