Posterior distribution with discrete priors
Computes the posterior distribution for an arbitrary one parameter distribution for a discrete prior distribution.
discrete.bayes(df,prior,y,...)
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 |
prob |
vector of posterior probabilities |
pred |
scalar with prior predictive probability |
Jim Albert
prior=c(.25,.25,.25,.25) names(prior)=c(.2,.25,.3,.35) y=5 n=10 discrete.bayes(dbinom,prior,y,size=n)
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