1 citations · 2 across the 5 of their papers we have counts for
5 papers
Besag-Clifford e-values for unnormalized testing
Alexander Dombowsky, Barbara E. Engelhardt, Aaditya Ramdas
Unnormalized probability distributions are frequently used in machine learning for modeling complex data generating processes. Though Markov chain Monte Carlo (MCMC) algorithms can…
Learning discrete Bayesian networks with hierarchical Dirichlet shrinkage
Alexander Dombowsky, David B. Dunson
A discrete Bayesian network is a directed acyclic graph (DAG) consisting of categorical variables. Two popular approaches for DBN modeling include classification and nonparametric…
Bayesian Learning of Clinically Meaningful Sepsis Phenotypes in Northern Tanzania
Alexander Dombowsky, David B. Dunson, Deng B. Madut +2
Sepsis is a life-threatening condition caused by a dysregulated host response to infection. Recently, researchers have hypothesized that sepsis consists of a heterogeneous spectrum…
Product Centered Dirichlet Processes for Bayesian Multiview Clustering
Alexander Dombowsky, David B. Dunson
While there is an immense literature on Bayesian methods for clustering, the multiview case has received little attention. This problem focuses on obtaining distinct but statistica…
Bayesian Clustering via Fusing of Localized Densities
Alexander Dombowsky, David B. Dunson
Bayesian clustering typically relies on mixture models, with each component interpreted as a different cluster. After defining a prior for the component parameters and weights, Mar…