rejectsampling

Implements a rejection sampling algorithm for a probability density
using a multivariate t proposal density

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

rejectsampling function

<dl> <dt>logf</dt>
<dd>function that defines the logarithm of the density of interest</dd> <dt>tpar</dt>
<dd>list of parameters of t proposal density including the mean m, scale matrix var, 
and degrees of freedom df</dd>
 
 <dt>dmax</dt>
<dd>logarithm of the rejection sampling constant</dd> <dt>n</dt>
<dd>number of simulated draws from proposal density</dd> <dt>data</dt>
<dd>data and or parameters used in the function logf</dd></dl>

Arguments

Author

Rejecting sampling using a t proposal density — rejectsampling

<dl>

 <dt>logf</dt>
<dd>function that defines the logarithm of the density of interest</dd>

 <dt>tpar</dt>
<dd>list of parameters of t proposal density including the mean m, scale matrix var, 
and degrees of freedom df</dd>
 
 <dt>dmax</dt>
<dd>logarithm of the rejection sampling constant</dd>

 <dt>n</dt>
<dd>number of simulated draws from proposal density</dd>

 <dt>data</dt>
<dd>data and or parameters used in the function logf</dd>

</dl>

rejectsampling: Rejecting sampling using a t proposal density

Description

Usage

Value

Arguments

Author

Examples