bradley.terry.post

Computes the log posterior density of the talent parameters and the log 
standard deviation for a Bradley Terry model with normal
random effects

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

bradley.terry.post function

<dl> <dt>theta</dt>
<dd>vector of talent parameters and log standard deviation</dd> <dt>data</dt>
<dd>data matrix with columns team1, team2, wins by team1, and
wins by team2</dd></dl>

Arguments

Author

Log posterior of a Bradley Terry random effects model — bradley.terry.post

<dl>

 <dt>theta</dt>
<dd>vector of talent parameters and log standard deviation</dd>

 <dt>data</dt>
<dd>data matrix with columns team1, team2, wins by team1, and
wins by team2</dd>

</dl>

bradley.terry.post: Log posterior of a Bradley Terry random effects model

Description

Usage

Value

Arguments

Author

Examples