11 citations · 23 across the 6 of their papers we have counts for
6 papers
Using Data Assimilation of Mechanistic Models to Estimate Glucose and Insulin Metabolism
Jami J. Mulgrave, Matthew E. Levine, David J. Albers +3
Motivation: There is a growing need to integrate mechanistic models of biological processes with computational methods in healthcare in order to improve prediction. We apply data a…
Bayesian Posterior Interval Calibration to Improve the Interpretability of Observational Studies
Jami J. Mulgrave, David Madigan, George Hripcsak
Observational healthcare data offer the potential to estimate causal effects of medical products on a large scale. However, the confidence intervals and p-values produced by observ…
The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records
Linying Zhang, Yixin Wang, Anna Ostropolets +3
The treatment effects of medications play a key role in guiding medical prescriptions. They are usually assessed with randomized controlled trials (RCTs), which are expensive. Rece…
Regression-Based Bayesian Estimation and Structure Learning for Nonparanormal Graphical Models
Jami J. Mulgrave, Subhashis Ghosal
A nonparanormal graphical model is a semiparametric generalization of a Gaussian graphical model for continuous variables in which it is assumed that the variables follow a Gaussia…
Bayesian Analysis of Nonparanormal Graphical Models Using Rank-Likelihood
Jami J. Mulgrave, Subhashis Ghosal
Gaussian graphical models, where it is assumed that the variables of interest jointly follow a multivariate normal distribution with a sparse precision matrix, have been used to st…
Bayesian Inference in Nonparanormal Graphical Models
Jami J. Mulgrave, Subhashis Ghosal
Gaussian graphical models have been used to study intrinsic dependence among several variables, but the Gaussianity assumption may be restrictive in many applications. A nonparanor…