We develop and evaluate an approach to causal modeling based on time series data, collectively referred to as“grouped graphical Granger modeling methods.” Graphical Granger mo...
Aurelie C. Lozano, Naoki Abe, Yan Liu, Saharon Ros...
It is probably fair to say that exact inference in graphical models is considered a solved problem, at least regarding its computational complexity: it is exponential in the treew...
Spatially-discrete Markov random fields (MRFs) and spatially-continuous variational approaches are ubiquitous in low-level vision, including image restoration, segmentation, opti...
Empirical modeling of the score distributions associated with retrieved documents is an essential task for many retrieval applications. In this work, we propose modeling the releva...
A dynamic model of a multiagent system defines a probability distribution over possible system behaviors over time. Alternative representations for such models present tradeoffs i...
Quang Duong, Michael P. Wellman, Satinder P. Singh...