howardprior

Computes the logarithm of a dependent prior on two proportions
proposed by Howard in a Statistical Science paper in 1998.

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

howardprior function

<dl> <dt>xy</dt>
<dd>vector of proportions p1 and p2</dd> <dt>par</dt>
<dd>vector containing parameter values alpha, beta, gamma, delta, sigma</dd></dl>

Arguments

Author

Logarithm of Howard's dependent prior for two proportions — howardprior

<dl>

 <dt>xy</dt>
<dd>vector of proportions p1 and p2</dd>

 <dt>par</dt>
<dd>vector containing parameter values alpha, beta, gamma, delta, sigma</dd>

</dl>

howardprior: Logarithm of Howard's dependent prior for two proportions

Description

Usage

Value

Arguments

Author

Examples