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\name{discrete.bayes.2}
\alias{discrete.bayes.2}
\alias{plot.bayes2}
\title{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{
\item{df}{name of the function defining the sampling density of
two parameters}
\item{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}
\item{y}{y is a matrix of data values, where each row corresponds to a
single observation}
\item{...}{any further fixed parameter values
used in the sampling density function}
}
\value{
\item{prob}{matrix of posterior probabilities}
\item{pred}{scalar with prior predictive probability}
}
\author{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)
}
\keyword{models}
|
