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

In LearnBayes: Functions for Learning Bayesian Inference

Description

Computes the parameters and mixing probabilities for a normal sampling problem, variance known, where the prior is a discrete mixture of normal densities.

Usage

normal.normal.mix(probs,normalpar,data)

Arguments

probs	vector of probabilities of the normal components of the prior
normalpar	matrix where each row contains the mean and variance parameters for a normal component of the prior
data	vector of observation and sampling variance

Value

probs	vector of probabilities of the normal components of the posterior
normalpar	matrix where each row contains the mean and variance parameters for a normal component of the posterior

Author(s)

Jim Albert

Examples

probs=c(.5, .5)
normal.par1=c(0,1)
normal.par2=c(2,.5)
normalpar=rbind(normal.par1,normal.par2)
y=1; sigma2=.5
data=c(y,sigma2)
normal.normal.mix(probs,normalpar,data)

Example output

$probs
normal.par1 normal.par2 
  0.4909845   0.5090155 

$normalpar
            post.mean  post.var
normal.par1 0.6666667 0.3333333
normal.par2 1.5000000 0.2500000

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