betabinexch

Computes the log posterior density of logit mean and log precision for a Binomial/beta exchangeable model

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

betabinexch function

<dl> <dt>theta</dt>
<dd>vector of parameter values of logit eta and log K</dd> <dt>data</dt>
<dd>a matrix with columns y (counts) and n (sample sizes)</dd></dl>

Arguments

Author

Log posterior of logit mean and log precision for Binomial/beta exchangeable model — betabinexch

<dl>

 <dt>theta</dt>
<dd>vector of parameter values of logit eta and log K</dd>

 <dt>data</dt>
<dd>a matrix with columns y (counts) and n (sample sizes)</dd>

</dl>

betabinexch: Log posterior of logit mean and log precision for Binomial/beta exchangeable model

Description

Usage

Value

Arguments

Author

Examples