Hex, Bugs and More Physics | Emre S. Tasci

a blog about physics, computation, computational physics and materials…

When you have some nice discrete random variables, or more correctly, some sets of nice discrete random variables and given that they behave well (like the ones you encounter all the time in the textbook examples), calculating the entropies and then moving on to the deducing of mutual information between these sets is never difficult. But things don’t happen that well in real situations: they tend to get nastier. The methods you employ for binary-valued sets are useless when you have 17000 different type of data among 25000 data points.

As an example, consider these two sets:



which one would you say is more ordered? A? Really? But how? — each of them has 5 different values which would amount to the same entropy for both. Yes, you can define two new sets based on these sets with actually having the differences between two adjacent members:

A’ = {1,1,1,1}

B’ = {120,-78,-31,88}

and the answer is obvious. This is really assuring – except the fact that it doesn’t have to be this obvious and furthermore, you don’t usually get only 6 membered sets to try out variations.

For this problem of mine, I have been devising a method and I think it’s working well. It’s really simple, you just bin (down) your data according some criteria and if it goes well, at the end you have classified your data points in much more smaller number of bins.

Today, I re-found the works of Dominik Endres and his colleagues. Their binning method is called the Bayesian Binning (you can find the detailed information at Endres’ personal page) and it pretty looks solid. I’ll still be focusing on my method (which I humbly call Tracking the Eskimo and the method itself compared to the Bayesian Binning, seems very primitive) but it’s always nice to see that, someone also had a similar problem and worked his way through it. There is also the other thing which I mention from time to time : when you treat your information as data, you get to have nice tools that were developed not by researchers from your field of interest but from totally different disciplines who were also at one time visited the 101010 world. 8) (It turns out that Dominik Endres and his fellows are from Psychology. One other thing is that I had begun thinking about my method after seeing another method for cytometry (the scientific branch which counts the cells in blood flow – or smt like that 8) applications…)


D. Endres and P. Földiák, Bayesian bin distribution inference and mutual information, IEEE Transactions on Information Theory, vol. 51, no. 11, pp. 3766-3779, November 2005 DOI: 10.1109/TIT.2005.856954

D. Endres and P. Földiák, Exact Bayesian Bin Classification: a fast alternative to Bayesian Classification and its application to neural response analysis,
Journal of Computational Neuroscience, 24(1): 24-35, 2008. DOI: 10.1007/s10827-007-0039-5.

D. Endres, M. Oram, J. Schindelin and P. Földiák, Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms,
pp. 393-400, Advances in Neural Information Processing Systems 20, MIT Press, Cambridge, MA, 2008 http://books.nips.cc/nips20.html

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