R: Simulation from Bayesian normal sampling model

normpostsim {LearnBayes} | R Documentation

## Simulation from Bayesian normal sampling model

### Description

Gives a simulated sample from the joint posterior distribution of the mean and variance for a normal sampling prior with a noninformative or informative prior. The prior assumes mu and sigma2 are independent with mu assigned a normal prior with mean mu0 and variance tau2, and sigma2 is assigned a inverse gamma prior with parameters a and b.

### Usage

    normpostsim(data,prior=NULL,m=1000)

### Arguments

`data` | vector of observations
`prior` | list with components mu, a vector with the prior mean and variance, and sigma2, a vector of the inverse gamma parameters
`m` | number of simulations desired

### Value

`mu` | vector of simulated draws of normal mean
`sigma2` | vector of simulated draws of normal variance

### Author(s)

Jim Albert

### Examples

    data(darwin)
    s=normpostsim(darwin$difference)

[Package LearnBayes version 2.15 Index]