6 papers · 1 filter
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.…
Multivariate Causal Effects: a Bayesian Causal Regression Factor Model
Dafne Zorzetto, Jenna Landy, Corwin Zigler +2
The impact of wildfire smoke on air quality is a growing concern, contributing to air pollution through a complex mixture of chemical species with important implications for public…
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…
Fast Variational Inference for Bayesian Factor Analysis in Single and Multi-Study Settings
Blake Hansen, Alejandra Avalos-Pacheco, Massimiliano Russo +1
Factors models are routinely used to analyze high-dimensional data in both single-study and multi-study settings. Bayesian inference for such models relies on Markov Chain Monte Ca…
Bayesian Combinatorial Multi-Study Factor Analysis
Isabella N. Grabski, Roberta De Vito, Lorenzo Trippa +1
Analyzing multiple studies allows leveraging data from a range of sources and populations, but until recently, there have been limited methodologies to approach the joint unsupervi…
Bayesian Ordinal Quantile Regression with a Partially Collapsed Gibbs Sampler
Isabella N Grabski, Roberta De Vito, Barbara E Engelhardt
Unlike standard linear regression, quantile regression captures the relationship between covariates and the conditional response distribution as a whole, rather than only the relat…