Jim — Sep 4, 2013, 9:36 PM
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# Discrete Prior for a Proportion
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# load in LearnBayes package
library(LearnBayes)
# construct the discrete prior
p <- seq(0.01, 0.99, by=0.01)
prior <- rep(1/99, 99)
# observe 5 successes, 10 failures
data <- c(5, 10)
# compute posterior probabilities
post <- pdisc(p, prior, data)
# line graph of posterior distribution
plot(p, post, type="h")
# construct matrix, columns are p and posterior probabilities
post.distribution <- cbind(p, post)
# find 90% probability interval
discint(post.distribution, 0.90)
$prob
[1] 0.9017
$set
[1] 0.17 0.18 0.19 0.20 0.21 0.22 0.23 0.24 0.25 0.26 0.27 0.28 0.29 0.30
[15] 0.31 0.32 0.33 0.34 0.35 0.36 0.37 0.38 0.39 0.40 0.41 0.42 0.43 0.44
[29] 0.45 0.46 0.47 0.48 0.49 0.50 0.51 0.52 0.53
#################################