1 citations · 1 across the 4 of their papers we have counts for
9 papers
Simulation-Based Empirical Bayes
Xinwei Shen, Diana Cai, Cheng Zhang +1
Empirical Bayes (EB) performs simultaneous inference across many related latent variables. Classical EB assumes that the likelihood p(x | z) is tractable. In many scientific applic…
Geometric Causal Models
Eli N. Weinstein, David M. Blei
Scientists often seek to draw causal inferences from structured data that is not independently and identically distributed, such as spatial data, network data, or molecular data. W…
Hierarchical Causal Models
Eli N. Weinstein, David M. Blei
Causal questions often arise in settings where data are hierarchical: subunits are nested within units. Consider students in schools, cells in patients, or cities in states. In the…
Bayesian Invariance Modeling of Multi-Environment Data
Luhuan Wu, Mingzhang Yin, Yixin Wang +2
Invariant prediction [Peters et al., 2016] analyzes feature/outcome data from multiple environments to identify invariant features - those with a stable predictive relationship to…
Adaptive Nonparametric Perturbations of Parametric Models with Generalized Bayes
Bohan Wu, Eli N. Weinstein, Sohrab Salehi +2
Parametric Bayesian modeling offers a powerful and flexible toolbox for machine learning. Yet the model, however detailed, may still be wrong, and this can make inferences untrustw…
Bayesian Empirical Bayes: Simultaneous Inference from Probabilistic Symmetries
Bohan Wu, Eli N. Weinstein, David M. Blei
Empirical Bayes (EB) improves the accuracy of simultaneous inference "by learning from the experience of others" (Efron, 2012). Classical EB theory focuses on latent variables that…