Joint Estimation and Inference for Multi-Experiment Networks of High-Dimensional Point Processes
@inproceedings{Wang2021JointEA, title={Joint Estimation and Inference for Multi-Experiment Networks of High-Dimensional Point Processes}, author={Xu Wang and Ali Shojaie}, year={2021} }
Modern high-dimensional point process data, especially those from neuroscience experiments, often involve observations from multiple conditions and/or experiments. Networks of interactions corresponding to these conditions are expected to share many edges, but also exhibit unique, condition-specific ones. However, the degree of similarity among the networks from different conditions is generally unknown. Existing approaches for multivariate point processes do not take these structures into…
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Joint Gaussian graphical model estimation: A survey
- Computer ScienceWIREs Computational Statistics
- 2022
This manuscript surveys recent work on statistical inference of joint Gaussian graphical models, identifying model structures that fit various data generation processes.
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