0th

Percentile

##### Gaussian Quadrature with Probability Distributions

Calculate nodes and weights for Gaussian quadrature in terms of probability distributions.

Keywords
math
##### Usage
gauss.quad.prob(n,dist="uniform",l=0,u=1,mu=0,sigma=1,alpha=1,beta=1)
##### Arguments
n

number of nodes and weights

dist

distribution that Gaussian quadrature is based on, one of "uniform", "normal", "beta" or "gamma"

l

lower limit of uniform distribution

u

upper limit of uniform distribution

mu

mean of normal distribution

sigma

standard deviation of normal distribution

alpha

positive shape parameter for gamma distribution or first shape parameter for beta distribution

beta

positive scale parameter for gamma distribution or second shape parameter for beta distribution

##### Details

This is a rewriting and simplification of gauss.quad in terms of probability distributions. The probability interpretation is explained by Smyth (1998). For details on the underlying quadrature rules, see gauss.quad.

The expected value of f(X) is approximated by sum(w*f(x)) where x is the vector of nodes and w is the vector of weights. The approximation is exact if f(x) is a polynomial of order no more than 2n-1. The possible choices for the distribution of X are as follows:

Uniform on (l,u).

Normal with mean mu and standard deviation sigma.

Beta with density x^(alpha-1)*(1-x)^(beta-1)/B(alpha,beta) on (0,1).

Gamma with density x^(alpha-1)*exp(-x/beta)/beta^alpha/gamma(alpha).

##### Value

A list containing the components

nodes

vector of values at which to evaluate the function

weights

vector of weights to give the function values

##### References

Smyth, G. K. (1998). Polynomial approximation. In: Encyclopedia of Biostatistics, P. Armitage and T. Colton (eds.), Wiley, London, pages 3425-3429. http://www.statsci.org/smyth/pubs/PolyApprox-Preprint.pdf

gauss.quad, integrate

##### Examples
# NOT RUN {
#  the 4th moment of the standard normal is 3
sum(out$weights * out$nodes^4)
sum(out$weights * log(out$nodes))