discrete.bayes: Posterior distribution with discrete priors

Description Usage Arguments Value Author(s) Examples

View source: R/discrete.bayes.R

Description

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

Usage

1

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

Author(s)

Jim Albert

Examples

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prior=c(.25,.25,.25,.25)
names(prior)=c(.2,.25,.3,.35)
y=5
n=10
discrete.bayes(dbinom,prior,y,size=n)

LearnBayes documentation built on May 30, 2017, 4:48 a.m.

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