Discrete Gaussian distributions via theta functions
Abstract
Maximum entropy probability distributions are important for information theory and relate directly to exponential families in statistics.
Having the property of maximizing entropy can be used to define a discrete analogue of the classical continuous Gaussian distribution.
We present a parametrization of such a density using the Riemann Theta function,
use it to derive fundamental properties and exhibit strong connections to the study of abelian varieties in algebraic geometry.
This is joint work with Carlos Amendola (TU Munich).
