R: Posterior distribution of two parameters with discrete priors

discrete.bayes.2 {LearnBayes} | R Documentation

## Posterior distribution of two parameters with discrete priors

### 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)

[Package LearnBayes version 2.15 Index]