Statistical learning with uncertainty quantification of large-scale causal networks
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Causal discovery aims to provide a plausible hypothesis about the causal mechanism. This work introduces statistical methods to learn and quantify the uncertainty of learning causal relationships in a directed acyclic graph in three different data scenarios.
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University of Minnesota Ph.D. dissertation. October 2022. Major: Statistics. Advisor: Xiaotong Shen. 1 computer file (PDF); ix, 161 pages.
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Li, Chunlin. (2022). Statistical learning with uncertainty quantification of large-scale causal networks. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/270055.
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