R: Computes the posterior for normal sampling and a mixture of...

normal.normal.mix {LearnBayes} | R Documentation

## 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)

[Package LearnBayes version 2.15 Index]