Timeline for How to estimate discrete probability distribution from a dataset of pairwise frequencies?
Current License: CC BY-SA 4.0
5 events
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| Jul 7, 2021 at 22:27 | history | edited | Ben | CC BY-SA 4.0 |
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| Jul 6, 2021 at 14:11 | comment | added | Ben | Thanks for the advice Lucas. I'll give this a try. | |
| Jul 6, 2021 at 12:07 | comment | added | Lucas Prates | A nice build up from your original idea. However, you are including constant terms such as $log(1/2\pi)$ on the optimization function, this is not necessary. Moreover, why is ${n \choose k}p^{k}(1-p)^{n-k}$ hard to compute? If you are referring to the term ${n \choose k}$, just take the log and it will be a constant as well (the $n$ does not matter, only your parameters). Optimizing this log-likelihood seems easier than using the normal approximation. The approximation might be poor if one of your probabilities is near the extremes of $[0,1]$, which might be the case for your example. | |
| Jul 5, 2021 at 19:49 | history | edited | Ben | CC BY-SA 4.0 |
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| Jul 5, 2021 at 19:43 | history | answered | Ben | CC BY-SA 4.0 |