mnormt.twosided

Bayesian test that a normal mean is equal to a specified
 value using a normal prior

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.twosided function

<dl> <dt>m0</dt>
<dd>value of the mean to be tested</dd> <dt>prob</dt>
<dd>prior probability of the hypothesis</dd> <dt>t</dt>
<dd>vector of values of the prior standard
 deviation under the alternative hypothesis</dd> <dt>data</dt>
<dd>vector containing the sample mean,
 the sample size, and the known value of the
 population standard deviation</dd></dl>

Arguments

Author

Bayesian test of a two-sided hypothesis about a normal mean — mnormt.twosided

<dl>

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

 <dt>prob</dt>
<dd>prior probability of the hypothesis</dd>

 <dt>t</dt>
<dd>vector of values of the prior standard
 deviation under the alternative hypothesis</dd>

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

</dl>

mnormt.twosided: Bayesian test of a two-sided hypothesis about a normal mean

Description

Usage

Value

Arguments

Author

Examples