discrete.bayes: Posterior distribution with discrete priors

In LearnBayes: Functions for Learning Bayesian Inference

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

Author(s)

Jim Albert

Examples

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

Example output

LearnBayes documentation built on May 1, 2019, 7:03 p.m.