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by jules
5667 days ago
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Say you have a prior probability distribution P(p) for the probability you think the coin is a coin that comes up heads with probability p. Your probability distribution P(p) will probably have a huge peak around p=0.5, but you can choose any prior belief. So P(p) is your opinion about the coin prior to seeing the experiment. Now we can apply Bayes' theorem to compute your opinion P'(p) about the coin after seeing the experiment: P'(p) = P(p | H)
= P(H | p)*P(p)/P(H)
= p*P(p)/integral(P(H | p)*P(p)dp)
= p*/E(P) * P(p)
Your belief that the coin has probability p is skewed by a factor of p/E(p).Here's an example of a graph of P(p) that shows how your belief about the coin is skewed after seeing a heads: http://dl.dropbox.com/u/388822/coin.png The first graph is an example of a prior belief about the coin, the second graph is the belief that this person should have after seeing the experiment. So the answer to the question is: P'(1) = 1/E(P) * P(1) = P(1)/E(P)
i.e. your new probability that this is a coin that always comes up heads is your old probability divided by your expected value of the probability of coming up heads.For example if your prior belief was unbiased, then E(P)=0.5, and P'(1) = 2*P(1). |
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