prior.two.parameters

Constructs a discrete uniform prior distribution for two parameters

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

prior.two.parameters function

<dl> <dt>parameter1</dt>
<dd>vector of values of first parameter</dd> <dt>parameter2</dt>
<dd>vector of values of second parameter</dd></dl>

Arguments

Author

Construct discrete uniform prior for two parameters — prior.two.parameters

<dl>

 <dt>parameter1</dt>
<dd>vector of values of first parameter</dd>

 <dt>parameter2</dt>
<dd>vector of values of second parameter</dd>

</dl>

prior.two.parameters: Construct discrete uniform prior for two parameters

Description

Usage

Value

Arguments

Author

Examples