Graphical models with corrupted data

Music City Center 

We consider the problem of estimating an undirected conditional independence graph. In many settings of interest, the process of interest is not observed directly. Instead, the recorded measurements are the process of interest corrupted by a nuissance process. In this setting, ignoring the nuissance process will result in many false positive and inconsistent estimation. In this talk, we show that, under certain assumptions, the conditional independence graph for the process of interest is still identifiable and can be estimated consistently.