Title: Learning stable graphical models
Authors: Sofia Massa - University of Oxford (United Kingdom) [presenting]
Abstract: Ongoing advances in model selection techniques for graphical models are trying to capture the structure of complex, high-dimensional datasets. Sparsity is usually invoked and techniques based on regularization, cross-validation, resampling and shrinkage estimation are becoming quite standard. One practical challenge in many applied contexts is how to assess the stability of different dependency structures and how to report the uncertainty associated with them. We will look at possible stability and uncertainty measures for undirected and chain graphs models.