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2011 Oct 17
1
Best practices for handling very small numbers?
Greetings
I have been experimenting with sampling from posterior distributions using
R. Assume that I have the following observations from a normal distribution,
with an unscaled joint likelihood function:
normsamples = rnorm(1000,8,3)
joint_likelihood = function(observations, mean, sigma){
return((sigma ^ (-1 * length(observations))) * exp(-0.5 * sum(
((observations - mean ) ^ 2)) / (sigma
2006 Dec 05
3
Comparing posterior and likelihood estimates for proportions (off topic)
This question is slightly off topic, but I'll use R to try and make it
as relevant as possible. I'm working on a problem where I want to
compare estimates from a posterior distribution with a uniform prior
with those obtained from a frequentist approach. Under these conditions
the estimates should agree.
Specifically, I am asking the question, "What is the probability that
the true