rigamma

Simulates from a inverse gamma (a, b) distribution with density 
proportional to $y^(-a-1) exp(-b/y)$

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

rigamma function

<dl> <dt>n</dt>
<dd>number of random numbers to be generated</dd> <dt>a</dt>
<dd>inverse gamma shape parameter</dd> <dt>b</dt>
<dd>inverse gamma rate parameter</dd></dl>

Arguments

Author

Random number generation for inverse gamma distribution — rigamma

<dl>

 <dt>n</dt>
<dd>number of random numbers to be generated</dd>

 <dt>a</dt>
<dd>inverse gamma shape parameter</dd>

 <dt>b</dt>
<dd>inverse gamma rate parameter</dd>

</dl>

rigamma: Random number generation for inverse gamma distribution

Description

Usage

Value

Arguments

Author

Examples