Take the 2-minute tour ×
Computer Science Stack Exchange is a question and answer site for students, researchers and practitioners of computer science. It's 100% free, no registration required.

I have frequency data for different events under two conditions, resulting in sets of frequencies F1 and F2. I would like to normalize the frequencies of events under condition 1 by their frequencies under condition 2. However, there are events that occur in condition 1 but not condition 2, resulting in divide-by-zero problems when I attempt to normalize.

For raw count data, I understand that there are a number of smoothing techniques (e.g. Witten-Bell) that can help sort this out, but I only have the frequencies, not the individual counts. In other words, I have frequencies like {0, 0.1, 0.2, 0.7} which could correspond to counts of {0, 1, 2, 7}, {0, 10, 20, 70}, etc. Are there any algorithms that are able to smooth this type of frequency data?

share|improve this question
    
I think this question is borderline between Computer Science and Cross Validated (but I confess to knowing nothing about stats and machine learning). If you don't get a good answer here after a couple of days, we can migrate your question to Cross Validated. Do not repost; if you wish your question to be migrated, flag it or reply to this comment. –  Gilles Jan 7 '13 at 23:43
add comment

1 Answer

up vote 0 down vote accepted

Yes. $\:$ Assume that the counts have the smallest sum that would produce your frequency data. $\:$ (How to do that depends on whether the frequencies were calculated and stored as doubles or something else.)

share|improve this answer
add comment

Your Answer

 
discard

By posting your answer, you agree to the privacy policy and terms of service.

Not the answer you're looking for? Browse other questions tagged or ask your own question.