The _second_ big thing is that [Susanne Still]( 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](

* Susanne Still, James P. Crutchfield and Christopher J. Ellison, [Optimal causal inference: estimating stored information and approximating causal architecture](

* Susanne Still and William Bialek, [How many clusters? an information-theoretic perspective](

* Susanne Still, [Information theoretic approach to interactive learning](

* Susanne Still, [Information bottleneck approach to predictive inference]( (Part of a [special issue of _Entropy_ on the information bottleneck method](

* Susanne Still, David A. Sivak, Anthony J. Bell and Gavin E. Crooks [The thermodynamics of prediction](

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.