Calculates an approximation to the expected value of any function of a normally-distributed random variable, using Gauss-Hermite quadrature.
gauss.hermite(f, mu = 0, sd = 1, ..., order = 5)Numeric value, vector or matrix.
The function whose moment should be approximated.
Mean of the normal distribution.
Standard deviation of the normal distribution.
Additional arguments passed to f.
Number of quadrature points in the Gauss-Hermite quadrature approximation. A small positive integer.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au
, Rolf Turner rolfturner@posteo.net
and Ege Rubak rubak@math.aau.dk.
This algorithm calculates the approximate expected value of
f(Z) when Z is a normally-distributed random
variable with mean mu and standard deviation sd.
The expected value is an integral with respect to the
Gaussian density; this integral is approximated
using Gauss-Hermite quadrature.
The argument f should be a function in the R language
whose first argument is the variable Z. Additional arguments
may be passed through .... The value returned by f
may be a single numeric value, a vector, or a matrix. The values
returned by f for different values of Z must have
compatible dimensions.
The result is a weighted average of several values of f.
gauss.hermite(function(x) x^2, 3, 1)
Run the code above in your browser using DataLab