# normal.normal.mix: Computes the posterior for normal sampling and a mixture of... In LearnBayes: Functions for Learning Bayesian Inference

## 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

 `1` ```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

Jim Albert

## Examples

 ```1 2 3 4 5 6 7``` ```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) ```

### Example output

```\$probs
normal.par1 normal.par2
0.4909845   0.5090155

\$normalpar
post.mean  post.var
normal.par1 0.6666667 0.3333333
normal.par2 1.5000000 0.2500000
```

LearnBayes documentation built on May 1, 2019, 7:03 p.m.