binomial.beta.mix

Computes the parameters and mixing probabilities for a binomial sampling problem
where the prior is a discrete mixture of beta densities.

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

binomial.beta.mix function

<dl> <dt>probs</dt>
<dd>vector of probabilities of the beta components of the prior</dd> <dt>betapar</dt>
<dd>matrix where each row contains the shape parameters for a beta component of the prior</dd> <dt>data</dt>
<dd>vector of number of successes and number of failures</dd></dl>

Arguments

Author

Computes the posterior for binomial sampling and a mixture of betas prior — binomial.beta.mix

<dl>

 <dt>probs</dt>
<dd>vector of probabilities of the beta components of the prior</dd>

 <dt>betapar</dt>
<dd>matrix where each row contains the shape parameters for a beta component of the prior</dd>

 <dt>data</dt>
<dd>vector of number of successes and number of failures</dd>

</dl>

binomial.beta.mix: Computes the posterior for binomial sampling and a mixture of betas prior

Description

Usage

Value

Arguments

Author

Examples