6 papers
Bayesian Analysis Using a Constrained Mixture of Normal-Inverse-Gamma Models
Madelyn Clinch, Jonathan R. Bradley, Andrés F. Barrientos +1
Gaussian mixtures of regressions are commonly implemented via a Gibbs sampler. This Markov chain Monte Carlo (MCMC) algorithm can be computationally burdensome because of the need…
Informed Asymmetric Dirichlet Priors for Multivariate Bernoulli Mixture Models
Luisa Ferrari, Maria Franco Villoria, Garritt L. Page +1
Clustering multivariate binary data is of interest in many scientific fields, including ecology, biomedicine, and social policy. Beyond heuristic clustering algorithms, such data c…
Uncertainty Quantification in Bayesian Clustering
Garritt L. Page, Andrés F. Barrientos, David B. Dahl +1
Bayesian clustering methods have the widely touted advantage of providing a probabilistic characterization of uncertainty in clustering through the posterior distribution. An amazi…
Latent Modularity in Multi-View Data
Andrea Cremaschi, Maria De Iorio, Garritt Page +1
In this article, we consider the problem of clustering multi-view data, that is, information associated to individuals that form heterogeneous data sources (the views). We adopt a…
Hybrid Geometry-Adaptive MCMC for Bayesian Inference in Higher-Order Ising Models
Godwin Osabutey, Robert Richardson, Garritt L. Page
We address the inverse problem for the mean-field Ising model with two- and three-body interactions using a Bayesian framework. Parameter recovery in this setting is notoriously di…
Informed Random Partition Models with Temporal Dependence
Sally Paganin, Garritt L. Page, Fernando Andrés Quintana
Model-based clustering is a powerful tool that is often used to discover hidden structure in data by grouping observational units that exhibit similar response values. Recently, cl…