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

In LearnBayes: Functions for Learning Bayesian Inference

Description

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

Usage

binomial.beta.mix(probs,betapar,data)

Arguments

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

Value

probs	vector of probabilities of the beta components of the posterior
betapar	matrix where each row contains the shape parameters for a beta component of the posterior

Author(s)

Jim Albert

Examples

probs=c(.5, .5)
beta.par1=c(15,5)
beta.par2=c(10,10)
betapar=rbind(beta.par1,beta.par2)
data=c(20,15)
binomial.beta.mix(probs,betapar,data)

Example output

$probs
beta.par1 beta.par2 
0.2917961 0.7082039 

$betapar
          [,1] [,2]
beta.par1   35   20
beta.par2   30   25

LearnBayes documentation built on May 1, 2019, 7:03 p.m.