normpostsim

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.

models

A collection of functions helpful in learning the basic tenets of Bayesian statistical inference.  It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions.  It contains MCMC algorithms for summarizing posterior distributions defined by the user.  It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.

Jim Albert

LearnBayes

Functions for Learning Bayesian Inference

normpostsim function

<dl> <dt>data</dt>
<dd>vector of observations</dd> <dt>prior</dt>
<dd>list with components mu, a vector with the prior mean 
 and variance, and sigma2, a vector of the inverse gamma parameters</dd> <dt>m</dt>
<dd>number of simulations desired</dd></dl>

Arguments

Author

Simulation from Bayesian normal sampling model — normpostsim

<dl>

 <dt>data</dt>
<dd>vector of observations</dd>

 <dt>prior</dt>
<dd>list with components mu, a vector with the prior mean 
 and variance, and sigma2, a vector of the inverse gamma parameters</dd>

 <dt>m</dt>
<dd>number of simulations desired</dd>

</dl>

normpostsim: Simulation from Bayesian normal sampling model

Description

Usage

Value

Arguments

Author

Examples