LearnBayes: normal.normal.mix – R documentation

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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

Author(s)

Jim Albert

Examples

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)

LearnBayes

Functions for Learning Bayesian Inference

v2.15.1

GPL (>= 2)

Authors