LearnBayes (version 2.15.1)

normal.normal.mix: Computes the posterior for normal sampling and a mixture of normals prior

Description

Computes the parameters and mixing probabilities for a normal sampling problem, variance known, where the prior is a discrete mixture of normal densities.

Usage

normal.normal.mix(probs,normalpar,data)

Arguments

probs

vector of probabilities of the normal components of the prior

normalpar

matrix where each row contains the mean and variance parameters for a normal component of the prior

data

vector of observation and sampling variance

Value

probs

vector of probabilities of the normal components of the posterior

normalpar

matrix where each row contains the mean and variance parameters for a normal component of the posterior

Examples

# NOT RUN {
probs=c(.5, .5)
normal.par1=c(0,1)
normal.par2=c(2,.5)
normalpar=rbind(normal.par1,normal.par2)
y=1; sigma2=.5
data=c(y,sigma2)
normal.normal.mix(probs,normalpar,data)
# }