beta.select

Finds the shape parameters of a beta density that matches knowledge of
two quantiles of the distribution.

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

beta.select function

<dl> <dt>quantile1</dt>
<dd>list with components p, the value of the first probability,
 and x, the value of the first quantile</dd> <dt>quantile2</dt>
<dd>list with components p, the value of the second probability,
 and x, the value of the second quantile</dd></dl>

Arguments

Author

Selection of Beta Prior Given Knowledge of Two Quantiles — beta.select

<dl>

 <dt>quantile1</dt>
<dd>list with components p, the value of the first probability,
 and x, the value of the first quantile</dd>

 <dt>quantile2</dt>
<dd>list with components p, the value of the second probability,
 and x, the value of the second quantile</dd>

</dl>

beta.select: Selection of Beta Prior Given Knowledge of Two Quantiles

Description

Usage

Value

Arguments

Author

Examples