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.

Functions for Learning Bayesian Inference

package-ubuntu-20.04-r-4.0.5#cran#LearnBayes-2.15.1

<p>Simulates a sample from a Dirichlet distribution
</p>

<table summary="R argblock">
<tr valign="top">
<td><code>n</code></td>
<td>
<p>number of simulations required</p>
</td>
</tr>
<tr valign="top">
<td><code>par</code></td>
<td>
<p>vector of parameters of the Dirichlet distribution</p>
</td>
</tr>
</table>

<p>matrix of simulated draws where each row corresponds to a single draw
</p>

package-ubuntu-20.04-r-4.0.5#cran#LearnBayes-2.15.1#docs#rdirichlet

rdirichlet

Description

Usage

Arguments

Value

Author(s)

Examples

LearnBayes

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`n`	number of simulations required
`par`	vector of parameters of the Dirichlet distribution