mnormt.onesided

Computes a Bayesian test of the hypothesis that a normal mean is 
less than or equal to a specified value

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

mnormt.onesided function

<dl> <dt>m0</dt>
<dd>value of the normal mean to be tested</dd> <dt>normpar</dt>
<dd>vector of mean and standard deviation of the normal prior distribution</dd> <dt>data</dt>
<dd>vector of sample mean, sample size, and known value of the population standard deviation</dd></dl>

Arguments

Author

Bayesian test of one-sided hypothesis about a normal mean — mnormt.onesided

<dl>

 <dt>m0</dt>
<dd>value of the normal mean to be tested</dd>

 <dt>normpar</dt>
<dd>vector of mean and standard deviation of the normal prior distribution</dd>

 <dt>data</dt>
<dd>vector of sample mean, sample size, and known value of the population standard deviation</dd>

</dl>

mnormt.onesided: Bayesian test of one-sided hypothesis about a normal mean

Description

Usage

Value

Arguments

Author

Examples