I see the point you’re making about observation selection effects but surely in this case it doesn’t flatten the posterior very much. Of all the times you see a coin come up heads 100 times in a row, most of them will be for coins with p(heads) close to 1, even if you are discarding all other runs. That’s assuming you select coins independently for each run.
Perhaps—obviously each coin is flipped just once, i.e. Binomial(n=1,p), which is the same thing as Bernoulli(p). I was trying to point out that for any other n it would work the same as a normal coin, if someone were to keep flipping it.
I see the point you’re making about observation selection effects but surely in this case it doesn’t flatten the posterior very much. Of all the times you see a coin come up heads 100 times in a row, most of them will be for coins with p(heads) close to 1, even if you are discarding all other runs. That’s assuming you select coins independently for each run.
Hmm perhaps I mis-read the post. I was assuming he was picking a single coin and flipping it 100 times.
The description of the coin flips having a Binomial(n=?,p) distribution, instead of a Bernoulli(p) distribution, might be a cause of the mis-reading.
Perhaps—obviously each coin is flipped just once, i.e. Binomial(n=1,p), which is the same thing as Bernoulli(p). I was trying to point out that for any other n it would work the same as a normal coin, if someone were to keep flipping it.