LearnBayes (version 2.15.1)

discrete.bayes: Posterior distribution with discrete priors

Description

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

Usage

discrete.bayes(df,prior,y,...)

Arguments

df

name of the function defining the sampling density

prior

vector defining the prior density; names of the vector define the parameter values and entries of the vector define the prior probabilities

y

vector of data values

...

any further fixed parameter values used in the sampling density function

Value

prob

vector of posterior probabilities

pred

scalar with prior predictive probability

Examples

# NOT RUN {
prior=c(.25,.25,.25,.25)
names(prior)=c(.2,.25,.3,.35)
y=5
n=10
discrete.bayes(dbinom,prior,y,size=n)
# }