% EXAMPLE M-FILES USING TEACHING BAYES TOOLBOX

%

%   Jim Albert - August 20, 1997

%

% Bayes' Rule

%   example1.m - Do you have a rare disease? (one observed outcome)

%   example2.m - Learning about a student's ability (sequential learning)

%

% Proportion Inference

%   example1.m - Inference using a discrete prior

%   example2.m - Summarizing a discrete posterior distributioin

%   example3.m - Prediction using a discrete prior  

%   example4.m - Assessing a beta prior

%   example5.m - Prediction using a beta prior

%   example6.m - A test for a proportion using a beta prior

%   example7.m - Inference using a histogram prior

%

% Two Proportion Inference

%   example1.m - Inference about 2 proportions using a discrete uniform prior

%   example2.m - Testing the equality of 2 proportions using a discrete prior   

%   example3.m - Inference about 2 proportions using a discrete subjective prior

%   example4.m - Testing the equality of 2 proportions using beta priors

%   example5.m - Inference about 2 proportions using an exchangeable prior

%

% Normal Mean Inference

%   example1.m - Inference about a mean (variance known) using a discrete prior

%   example2.m - Inference about a mean using a noninformative continuous prior

%   example3.m - Assessing a normal prior

%   example4.m - A test for a mean (variance known) using a normal prior

%

% Relationships

%   example1.m - Normal linear regression using a vague prior

%   example2.m - A Bayesian test of independence in a two-way contingency table

%

% Discrete models inference

%   example1.m - Learning about a Poisson mean

%   example2.m - Learning about the upper bound of a discrete uniform population

%   example3.m - Learning about the number of successes in a finite population

%   example4.m - Comparing two priors for a binomial proportion by a Bayes' factor

%

% Continuous models inference

%   example1.m - Estimating the size of a population by capture/recapture sampling

%   example2.m - Estimating a binomial proportion using a mixture prior

%

% Summarization of a Posterior Distribution

%   example1.m - Illustrates the use of Laplace, adaptive quadrature, and metropolis

%                algorithms for summarizing a one parameter posterior density.

%   example2.m - Illustrates the use of Laplace, adaptive quadrature, and Gibbs sampling

%                algorithms for summarizing a two parameter posterior density