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Bayesian Nonparametric Causal Inference for High-Dimensional Nutritional Data via Factor-Based Exposure Mapping
Dafne Zorzetto, Zizhao Xie, Julian Stamp +2
Diet plays a crucial role in health, and understanding the causal effects of dietary patterns is essential for informing public health policy and personalized nutrition strategies.…
Estimating Gaussian graphical models of multi-study data with Multi-Study Factor Analysis
Katherine H. Shutta, Denise M. Scholtens, William L. Lowe +2
Network models are powerful tools for gaining new insights from complex biological data. Most lines of investigation in biology involve comparing datasets in the setting where the…
Zero-Inflated Bayesian Multi-Study Infinite Non-Negative Matrix Factorization
Blake Hansen, Dafne Zorzetto, Valeria Edefonti +1
Understanding the association between dietary patterns and health outcomes, such as the cancer risk, is crucial to inform public health guidelines and shaping future dietary interv…
Missing data imputation using a truncated Gaussian infinite factor model with application to metabolomics data
Kate Finucane, Lorraine Brennan, Roberta De Vito +2
Metabolomics is the study of small molecules in biological samples. Metabolomics data are typically high-dimensional and contain highly correlated variables and frequent missing va…
Causal Inference for Latent Outcomes Learned with Factor Models
Jenna M. Landy, Dafne Zorzetto, Roberta De Vito +1
In many fields$\unicode{x2013}$including genomics, epidemiology, natural language processing, social and behavioral sciences, and economics$\unicode{x2013}$it is increasingly impor…
Sparse Bayesian Factor Models with Mass-Nonlocal Factor Scores
Yingjie Huang, Dafne Zorzetto, Roberta De Vito
Bayesian factor models are widely used for dimensionality reduction and pattern discovery in high-dimensional datasets across diverse fields. These models typically focus on imposi…