I've worked with the mutual information for some time. But I found a very recent measure in the "correlation world" that can also be used to measure distribution independence, the so called "distance correlation" ( also termed Brownian correlation): http://en.wikipedia.org/wiki/Brownian_covariance. I checked the papers where this measure is introduced, but without finding any allusion to the mutual information.

So, my questions are:

  • Do they solve exactly the same problem? If not, how the problems are different?
  • And if the previous question can be answered on the positive, what are the advantages of using one or the other?
  • $\begingroup$ Try to write down explicitly 'distance correlation' and 'mutual information' for a simple example. In the second case you will get logarithms, while in the first - not. $\endgroup$ – Piotr Migdal Jan 10 '12 at 11:52
  • $\begingroup$ @PiotrMigdal Yes, I'm aware of that difference. Could you please explain why is it important? Please, take into account that I'm not a statistician... $\endgroup$ – dsign Jan 10 '12 at 11:56
  • $\begingroup$ For ma a standard tool measuring mutual dependence of probability distributions is the mutual information. It has a lot of nice properties and its interpretation is straightforward. However, there may be specific problems where distance correlation is preferred (but I have never used it in my life). So what is the problem you are trying to solve? $\endgroup$ – Piotr Migdal Jan 10 '12 at 14:53
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    $\begingroup$ This comment is a few years late but Columbia University's Statistics Dept made the academic year 2013-2014 a year of focus on measures of dependence. In April-May 2014, a workshop was held that brought together the top academics doing work in this field including the Reshef Brothers (MIC), Gabor Szekely (distance correlations), Subhadeep Mukhopadhay to name a few. Here's a link to the program that includes many pdfs from the presentations. dependence2013.wikischolars.columbia.edu/… $\endgroup$ – Mike Hunter Oct 15 '15 at 11:24

Information / mutual information does not depend on the possible values, it depends only on the probabilities therefore it is less sensitive. Distance correlation is more powerful and simpler to compute. For a comparision see


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    $\begingroup$ Hi, thanks for your answer! The paper you refer to is about MIC, which is I'm believe is a bit more than MI. I have implemented the distance correlation measure and I don't think it be simpler than the MI for the elemental case of discrete categorical variables. Then again one thing that I have learned is that DCM is well defined and well behaved for continuous variables, but with MI you need to do binning or fancy stuff ala MIC. $\endgroup$ – dsign Feb 24 '12 at 14:05
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    $\begingroup$ However, DCM seems to need square matrices whose side is the number of samples. In other words, space complexity scales quadratically. Or at least that's my impression, I would like to be in a mistake. MIC does better, because you can tune it in some sort of compromise between precision and performance. $\endgroup$ – dsign Feb 24 '12 at 14:14

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