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Ann Arbor MI 02-01-2018 02-01-2018

We propose the combinatorial inference to explore the topological structures of graphical models. The combinatorial inference can conduct the hypothesis tests on many graph properties including connectivity, hub detection, perfect matching, etc. On the other side, we also develop a generic minimax lower bound which shows the optimality of the proposed method for a large family of graph properties. Our methods are applied to the neuroscience by discovering hub voxels contributing to visual memories.

Combinatorial Inference

Junwei Lu, Graduate Student in Operations Research and Financial Engineering

icon to add this event to your google calendarFebruary 1, 2018
3:30 PM - 5:00 PM
1690 SPH I
1415 Washington Heights
Ann Arbor, MI 48109-2029

Sponsored by: Department of Biostatistics Seminar
Contact Information: Zhenke Wu (zhenkewu@umich.edu)

We propose the combinatorial inference to explore the topological structures of graphical models. The combinatorial inference can conduct the hypothesis tests on many graph properties including connectivity, hub detection, perfect matching, etc. On the other side, we also develop a generic minimax lower bound which shows the optimality of the proposed method for a large family of graph properties. Our methods are applied to the neuroscience by discovering hub voxels contributing to visual memories.