R: Computes the posterior for Poisson sampling and a mixture of...

poisson.gamma.mix {LearnBayes} | R Documentation

## Computes the posterior for Poisson sampling and a mixture of gammas prior

### Description

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

### Usage

    poisson.gamma.mix(probs,gammapar,data)

### Arguments

`probs` | vector of probabilities of the gamma components of the prior
`gammapar` | matrix where each row contains the shape and rate parameters for a gamma component of the prior
`data` | list with components y, vector of counts, and t, vector of time intervals

### Value

`probs` | vector of probabilities of the gamma components of the posterior
`gammapar` | matrix where each row contains the shape and rate parameters for a gamma component of the posterior

### Author(s)

Jim Albert

### Examples

    probs=c(.5, .5)
    gamma.par1=c(1,1)
    gamma.par2=c(10,2)
    gammapar=rbind(gamma.par1,gamma.par2)
    y=c(1,3,2,4,10); t=c(1,1,1,1,1)
    data=list(y=y,t=t)
    poisson.gamma.mix(probs,gammapar,data)

[Package LearnBayes version 2.15 Index]