Correlation and Mutual Information

Peter Krafft Statistics Leave a Comment

[latexpage] Mutual information is a quantification of the dependency between random variables. It is sometimes contrasted with linear correlation since mutual information captures nonlinear dependence. In this short note I will discuss the relationship between these quantities in the case of a bivariate Gaussian distribution, and I will explore two implications of that relationship.

Asymptotic Equipartition of Markov Chains

Peter Krafft Statistics Leave a Comment

[latexpage] The Asymptotic Equipartition Property/Principle (AEP) is a well-known result that is likely covered in any introductory information theory class. Nevertheless, when I first learned about it in such a course, I did not appreciate the implications of its general form.  In this post I will review this beautiful, classic result and offer the mental picture I have of its implications. I will frame my discussion in terms of Markov chains with discrete state spaces, but note that the AEP holds even more generally. My treatment will be relatively informal, and I will assume basic familiarity with Markov chains. See the references for more details.