Bayesian Linear Modeling in High Dimensions: Advances in Hierarchical Modeling, Inference, and Evaluation
Across the sciences, social sciences and engineering, applied statisticians seek to build understandings of complex relationships from increasingly large datasets. In statistical genetics, for example, we observe up to millions of genetic variations in each of thousands of individuals, and wish to a...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/144554 |