89 citations · 297 across the 20 of their papers we have counts for
10 papers · 1 filter
Principal variables analysis for non-Gaussian data
Dylan Clark-Boucher, Jeffrey W. Miller
Principal variables analysis (PVA) is a technique for selecting a subset of variables that capture as much of the information in a dataset as possible. Existing approaches for PVA…
Bayesian data selection
Eli N. Weinstein, Jeffrey W. Miller
Insights into complex, high-dimensional data can be obtained by discovering features of the data that match or do not match a model of interest. To formalize this task, we introduc…
Bayesian Optimal Experimental Design for Inferring Causal Structure
Michele Zemplenyi, Jeffrey W. Miller
Inferring the causal structure of a system typically requires interventional data, rather than just observational data. Since interventional experiments can be costly, it is prefer…
Inference in generalized bilinear models
Jeffrey W. Miller, Scott L. Carter
Latent factor models are widely used to discover and adjust for hidden variation in modern applications. However, most methods do not fully account for uncertainty in the latent fa…
Consistent Model-based Clustering: using the Quasi-Bernoulli Stick-Breaking Process
Cheng Zeng, Jeffrey W. Miller, Leo L. Duan
In mixture modeling and clustering applications, the number of components and clusters is often not known. A stick-breaking mixture model, such as the Dirichlet process mixture mod…
Reproducible Model Selection Using Bagged Posteriors
Jonathan H. Huggins, Jeffrey W. Miller
Bayesian model selection is premised on the assumption that the data are generated from one of the postulated models. However, in many applications, all of these models are incorre…
Robust Inference and Model Criticism Using Bagged Posteriors
Jonathan H. Huggins, Jeffrey W. Miller
Standard Bayesian inference is known to be sensitive to model misspecification, leading to unreliable uncertainty quantification and poor predictive performance. However, finding g…
Flexible Models for Microclustering with Application to Entity Resolution
Giacomo Zanella, Brenda Betancourt, Hanna Wallach +3
Most generative models for clustering implicitly assume that the number of data points in each cluster grows linearly with the total number of data points. Finite mixture models, D…
Microclustering: When the Cluster Sizes Grow Sublinearly with the Size of the Data Set
Jeffrey Miller, Brenda Betancourt, Abbas Zaidi +2
Most generative models for clustering implicitly assume that the number of data points in each cluster grows linearly with the total number of data points. Finite mixture models, D…
Robust Bayesian inference via coarsening
Jeffrey W. Miller, David B. Dunson
The standard approach to Bayesian inference is based on the assumption that the distribution of the data belongs to the chosen model class. However, even a small violation of this…