robustt

Implements Gibbs sampling for a robust t sampling model with location mu, scale sigma, and degrees of freedom v

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

robustt function

<dl> <dt>y</dt>
<dd>vector of data values</dd> <dt>v</dt>
<dd>degrees of freedom for t model</dd>
 
 <dt>m</dt>
<dd>the number of cycles of the Gibbs sampler</dd></dl>

Arguments

Author

Gibbs sampling for a robust regression model — robustt

<dl>

 <dt>y</dt>
<dd>vector of data values</dd>

 <dt>v</dt>
<dd>degrees of freedom for t model</dd>
 
 <dt>m</dt>
<dd>the number of cycles of the Gibbs sampler</dd>

</dl>

robustt: Gibbs sampling for a robust regression model

Description

Usage

Value

Arguments

Author

Examples