Maximum Entropy in the framework of Algebraic Statistics: A First Step

Algebraic statistics is a recently evolving field, where one would treat statistical models as algebraic objects and thereby use tools from computational commutative algebra and algebraic geometry in the analysis and computation of statistical models. In this approach, calculation of parameters of statistical models amounts to solving set of polynomial equations in several variables, for which one can use celebrated Grobner basis theory. Owing to the important role of information theory in statistics, this paper as a first step, explores the possibility of describing maximum and minimum entropy (ME) models in the framework of algebraic statistics. We show that ME-models are toric models (a class of algebraic statistical models) when the constraint functions (that provide the information about the underlying random variable) are integer valued functions, and maximum entropy distributions can be calculated by solving set of (Laurent) polynomial equations when expected values of constraint functions are supplied as sample means.