betabinexch0: Log posterior of mean and precision for Binomial/beta...

In LearnBayes: Functions for Learning Bayesian Inference

Description

Computes the log posterior density of mean and precision for a Binomial/beta exchangeable model

Usage

betabinexch0(theta,data)

Arguments

theta	vector of parameter values of eta and K
data	a matrix with columns y (counts) and n (sample sizes)

Value

value of the log posterior

Author(s)

Jim Albert

Examples

n=c(20,20,20,20,20)
y=c(1,4,3,6,10)
data=cbind(y,n)
theta=c(.1,10)
betabinexch0(theta,data)

Example output

[1] -58.82094

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