discrete.bayes.2: Posterior distribution of two parameters with discrete priors

In bayesball/LearnBayes: Learning Bayesian Inference

Description

Computes the posterior distribution for an arbitrary two parameter distribution for a discrete prior distribution.

Usage

discrete.bayes.2(df,prior,y=NULL,...)

Arguments

df	name of the function defining the sampling density of two parameters
prior	matrix defining the prior density; the row names and column names of the matrix define respectively the values of parameter 1 and values of parameter 2 and the entries of the matrix give the prior probabilities
y	y is a matrix of data values, where each row corresponds to a single observation
...	any further fixed parameter values used in the sampling density function

Value

prob	matrix of posterior probabilities
pred	scalar with prior predictive probability

Author(s)

Jim Albert

Examples

p1 = seq(0.1, 0.9, length = 9)
p2 = p1
prior = matrix(1/81, 9, 9)
dimnames(prior)[[1]] = p1
dimnames(prior)[[2]] = p2
discrete.bayes.2(twoproplike,prior)

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