89 citations · 297 across the 20 of their papers we have counts for
22 papers
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…
Strong uniform laws of large numbers for bootstrap means and other randomly weighted sums
Neil A. Spencer, Jeffrey W. Miller
This article establishes novel strong uniform laws of large numbers for randomly weighted sums such as bootstrap means. By leveraging recent advances, these results extend previous…
Consistency of mixture models with a prior on the number of components
Jeffrey W. Miller
This article establishes general conditions for posterior consistency of Bayesian finite mixture models with a prior on the number of components. That is, we provide sufficient con…
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…
Asymptotic normality, concentration, and coverage of generalized posteriors
Jeffrey W. Miller
Generalized likelihoods are commonly used to obtain consistent estimators with attractive computational and robustness properties. Formally, any generalized likelihood can be used…
Fast and accurate approximation of the full conditional for gamma shape parameters
Jeffrey W. Miller
The gamma distribution arises frequently in Bayesian models, but there is not an easy-to-use conjugate prior for the shape parameter of a gamma. This inconvenience is usually dealt…
A detailed treatment of Doob's theorem
Jeffrey W. Miller
Doob's theorem provides guarantees of consistent estimation and posterior consistency under very general conditions. Despite the limitation that it only guarantees consistency on a…
An elementary derivation of the Chinese restaurant process from Sethuraman's stick-breaking process
Jeffrey W. Miller
The Chinese restaurant process (CRP) and the stick-breaking process are the two most commonly used representations of the Dirichlet process. However, the usual proof of the connect…
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…
Inconsistency of Pitman-Yor process mixtures for the number of components
Jeffrey W. Miller, Matthew T. Harrison
In many applications, a finite mixture is a natural model, but it can be difficult to choose an appropriate number of components. To circumvent this choice, investigators are incre…
Importance sampling for weighted binary random matrices with specified margins
Matthew T. Harrison, Jeffrey W. Miller
A sequential importance sampling algorithm is developed for the distribution that results when a matrix of independent, but not identically distributed, Bernoulli random variables…
A simple example of Dirichlet process mixture inconsistency for the number of components
Jeffrey W. Miller, Matthew T. Harrison
For data assumed to come from a finite mixture with an unknown number of components, it has become common to use Dirichlet process mixtures (DPMs) not only for density estimation,…
Exact sampling and counting for fixed-margin matrices
Jeffrey W. Miller, Matthew T. Harrison
The uniform distribution on matrices with specified row and column sums is often a natural choice of null model when testing for structure in two-way tables (binary or nonnegative…
Reduced Criteria for Degree Sequences
Jeffrey W. Miller
For many types of graphs, criteria have been discovered that give necessary and sufficient conditions for an integer sequence to be the degree sequence of such a graph. These crite…
Exact Enumeration and Sampling of Matrices with Specified Margins
Jeffrey W. Miller, Matthew T. Harrison
We describe a dynamic programming algorithm for exact counting and exact uniform sampling of matrices with specified row and column sums. The algorithm runs in polynomial time when…