rmnorm

Simulates from a multivariate normal distribution

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

rmnorm function

<dl> <dt>n</dt>
<dd>number of random numbers to be generated</dd> <dt>mean</dt>
<dd>numeric vector giving the mean of the distribution</dd> <dt>varcov</dt>
<dd>a positive definite matrix representing the variance-covariance matrix of the distribution</dd></dl>

Arguments

Author

Random number generation for multivariate normal — rmnorm

<dl>

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

 <dt>mean</dt>
<dd>numeric vector giving the mean of the distribution</dd>

 <dt>varcov</dt>
<dd>a positive definite matrix representing the variance-covariance matrix of the distribution</dd>

</dl>

rmnorm: Random number generation for multivariate normal

Description

Usage

Value

Arguments

Author

Examples