PGM: Penalized Gaussian Mixtures

In SunJinglan/StatComp21080: Density estimations using Lindsey's method and penalized Gaussian mixtures

Description

A density estimation using Penalized Gaussian Mixtures method with Gibbs sampling and Hamiltonian Monte Carlo sampling

Usage

PGM(x, K = 35, a0 = min(x), b0 = max(x), iter = 5000, burnin = 1000)

Arguments

x	the sample to be estimated
K	the number of Gaussian distributions to be cumulated
a0	the minimum of the range to estimate the density
b0	the maximum of the range to estimate the density
iter	the number of Gibbs samples
burnin	the number of between-sample random numbers

Value

a list where t represents the possilble values and pgm represents the density of that value

Examples

## Not run: 
rnR <- gibbsR(100,10)
par(mfrow=c(2,1));
plot(rnR[,1],type='l')
plot(rnR[,2],type='l')

## End(Not run)

SunJinglan/StatComp21080 documentation built on Dec. 24, 2021, 1:24 a.m.