(New page: "I think you start by working the maximum likelihood estimation formula of a binomial RV. The number of photons captured is (1,000,000) and the probability of the camera catching a photon ...) |
|||
| Line 6: | Line 6: | ||
But to find the maximum I think you have to take the derivative of an n!... Does anyone know how to do this? Or am I going about the problem completely wrong?" | But to find the maximum I think you have to take the derivative of an n!... Does anyone know how to do this? Or am I going about the problem completely wrong?" | ||
| + | |||
| + | |||
Quote from Beau, here is what we needed to do. | Quote from Beau, here is what we needed to do. | ||
Revision as of 11:52, 10 November 2008
"I think you start by working the maximum likelihood estimation formula of a binomial RV. The number of photons captured is (1,000,000) and the probability of the camera catching a photon is p, n (the number of photons total) is what we are looking for.
$ \hat n_{ML} = \text{max}_n ( \binom{n}{k} p^{k} (1-p)^{n-k} ) $
$ \hat n_{ML} = \text{max}_n ( \binom{n}{1000000} p^{1000000} (1-p)^{n-1000000} ) $
But to find the maximum I think you have to take the derivative of an n!... Does anyone know how to do this? Or am I going about the problem completely wrong?"
Quote from Beau, here is what we needed to do.
1st) we know that for n+1 must be smaller than n
2nd) same thing we know from n-1 must be greater than n
then from sandwich theorem, obtain a inequality with n+1<n<n+1
solve for n.
