Yeah, they provide a lot of jumbled up text. Big picture:
posterior probability = prior probability * likelihood of outcome.
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I cant get my head round the following exercise:
You are testing dice for a casino to make sure that sixes do not come up more frequently than expected. Because you do not want to manually roll dice all day, you design a machine to roll a die repeatedly and record the number of sixes that come face up. In order to do a Bayesian analysis to test the hypothesis that p = 1/6 versus p = .175 , you set the machine to roll the die 6000 times. When you come back at the end of the day, you discover to your horror that the machine was unable to count higher than 999. The machine says that 999 sixes occurred. Given a prior probability of 0.8 placed on the hypothesis p = 1/6 , what is the posterior probability that the die is fair, given the censored data? Hint - to find the probability that at least x sixes occurred in N trials with proportion p (which is the likelihood in this problem), use the R command :
1-pbinom(x-1,N,p)
The possible answers are 0.5, 0.684, 0.8 or 0.881.
I would really appreciate if someone could help me here! I need to understand this approach!
Cheers and thanks in advance! Markus
Yeah, they provide a lot of jumbled up text. Big picture:
posterior probability = prior probability * likelihood of outcome.
Stop cowardice, ban guns!
Most of Bayes exercise consisted of shooting pool...
what is the likelihood...a probability, an estimator?
"Very few theories have been abandoned because they were found to be invalid on the basis of empirical evidence...." Spanos, 1995
Dason, please make the Purdy open infinity sign (proportionality) for me and I will correct all that is wrong
Thanks.
Stop cowardice, ban guns!
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