|
Abstract:
|
Networks and other large sparse data sets pose significant challenges for statistical inference, as many standard statistical methods for testing model fit are not applicable in such settings. Algebraic statistics offers an approach to goodness-of-fit testing that relies on understanding the geometric and algebraic properties of the model. In this talk, we will survey the current state-of-the-art in statistical inference for network models using algebraic tools and software, focusing on a log-linear network models, a subclass of exponential random graph models.
|
