The _second_ big thing is that [Susanne Still](http://www2.hawaii.edu/~sstill/) and a grad student of hers have written software that uses similar information-theoretic ideas to make predictions of time series given past data and to estimate _the optimal model given constraints on how much information the model can use_. Even better, my student [[Blake Pollard]] is working with her to apply this software to El Niño data!

He will start with a simple demonstration just to help her write a paper on this subject. But we may expand this to a larger project: to study Niño and other climate phenomena using ideas from information theory!

This is very nice because again it's compatible with a lot of things I already want to do, and things the Azimuth Code Project is starting to do... and it begins to _connect_ some of these ideas.

Here is some reading material suggested by Susanne:

* Naftali Tishby, Fernando C. Pereira and William Bialek, [The information bottleneck method](http://www.cnbc.cmu.edu/cns/papers/Tishby-NC-1999.pdf).

* Susanne Still, James P. Crutchfield and Christopher J. Ellison, [Optimal causal inference: estimating stored information and approximating causal architecture](http://arxiv.org/abs/0708.1580).

* Susanne Still and William Bialek, [How many clusters? an information-theoretic perspective](http://www2.hawaii.edu/~sstill/HowManyClusters.pdf).

* Susanne Still, [Information theoretic approach to interactive learning](http://www2.hawaii.edu/~sstill/Still_IL2009_EPL.pdf).

* Susanne Still, [Information bottleneck approach to predictive inference](http://www.mdpi.com/1099-4300/16/2/968). (Part of a [special issue of _Entropy_ on the information bottleneck method](http://www.mdpi.com/journal/entropy/special_issues/bottleneck-method).)

* Susanne Still, David A. Sivak, Anthony J. Bell and Gavin E. Crooks [The thermodynamics of prediction](http://arxiv.org/abs/1203.3271).

If you think you're getting bombarded with too many references, don't worry! I will read a bunch of this stuff and explain it on the blog. You just need to read the blog articles. (I know, that's already hard enough!)

Anyway, I'm very excited.