2 citations · 4 across the 6 of their papers we have counts for
6 papers
Optimality-based reward learning with applications to toxicology
Samuel J. Weisenthal, Matthew Eckard, Askhan Ertefaie +2
In toxicology research, experiments are often conducted to determine the effect of toxicant exposure on the behavior of mice, where mice are randomized to receive the toxicant or n…
Testing Sparsity Assumptions in Bayesian Networks
Luke Duttweiler, Sally W. Thurston, Anthony Almudevar
Bayesian network (BN) structure discovery algorithms typically either make assumptions about the sparsity of the true underlying network, or are limited by computational constraint…
Inference for relative sparsity
Samuel J. Weisenthal, Sally W. Thurston, Ashkan Ertefaie
In healthcare, there is much interest in estimating policies, or mappings from covariates to treatment decisions. Recently, there is also interest in constraining these estimated p…
Relative Sparsity for Medical Decision Problems
Samuel J. Weisenthal, Sally W. Thurston, Ashkan Ertefaie
Existing statistical methods can estimate a policy, or a mapping from covariates to decisions, which can then instruct decision makers (e.g., whether to administer hypotension trea…
Spectral Bayesian Network Theory
Luke Duttweiler, Sally W. Thurston, Anthony Almudevar
A Bayesian Network (BN) is a probabilistic model that represents a set of variables using a directed acyclic graph (DAG). Current algorithms for learning BN structures from data fo…
Selection of inverse gamma and half-t priors for hierarchical models: sensitivity and recommendations
Zachary Brehm, Aaron Wagner, Erik VonKaenel +5
While the importance of prior selection is well understood, establishing guidelines for selecting priors in hierarchical models has remained an active, and sometimes contentious, a…