Timeline for Characterizing/Fitting Word Count Data into Zipf / Power Law / LogNormal
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
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| Feb 26, 2019 at 19:23 | comment | added | ivangtorre | The slope in your data looks to be 1.66 (10/6). I think that your fitting is not working well | |
| Mar 12, 2018 at 20:44 | history | bounty ended | CommunityBot | ||
| Mar 5, 2018 at 14:52 | comment | added | David Dale | @born_to_hula, if you mean the value 0.5366, it is just the parameter of Zipf distribution, just like mean and variance for Normal distribution, or mean (lambda) for Poisson, or p and r for Negative binomial. To understand how I obtained it, you can read the Wikipedia articles on Zipf law and on MLE. | |
| Mar 5, 2018 at 14:50 | comment | added | born to hula | Thanks for your answer - this looks promising! Could you please explain or point me to some material on how to interpret the result you obtained above in regards to calculating the neg_zipf_likelihood? | |
| Mar 5, 2018 at 11:10 | history | edited | David Dale | CC BY-SA 3.0 |
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| Mar 4, 2018 at 20:17 | history | answered | David Dale | CC BY-SA 3.0 |