betabinexch: Log posterior of logit mean and log precision for...

In LearnBayes: Functions for Learning Bayesian Inference

Description

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

Usage

betabinexch(theta,data)

Arguments

theta	vector of parameter values of logit eta and log 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(-1,0)
betabinexch(theta,data)

Example output

[1] -58.4339

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