poisson.gamma.mix: Computes the posterior for Poisson sampling and a mixture of...

In bayesball/LearnBayes: Learning Bayesian Inference

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)

bayesball/LearnBayes documentation built on May 11, 2019, 9:21 p.m.