6 citations · 6 across the 1 of their papers we have counts for
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Bayesian modeling of nearly mutually orthogonal processes
James Matuk, Amy H. Herring, David B. Dunson
Functional factor analysis is an important dimension reduction method for functional and longitudinal data. Factor loadings give insight into patterns of variability of the observa…
Posterior Projection for Inference in Constrained Spaces
Lachlan Astfalck, Deborshee Sen, Sayan Patra +2
Estimation of parameters that obey specific constraints is crucial in statistics and machine learning; for example, when parameters are required to satisfy boundedness, monotonicit…
Using prior information to boost power in correlation structure support recovery
Ziyang Ding, David Dunson
Hypothesis testing of structure in correlation and covariance matrices is of broad interest in many application areas. In high dimensions and/or small to moderate sample sizes, hig…
Scalable Bayesian inference for time series via divide-and-conquer
Rihui Ou, Lachlan Astfalck, Deborshee Sen +1
Bayesian computational algorithms tend to scale poorly as data size increases. This has motivated divide-and-conquer-based approaches for scalable inference. These divide the data…
Identifiable and interpretable nonparametric factor analysis
Maoran Xu, Steven Winter, Amy H. Herring +1
Factor models are widely used to reduce dimensionality in modeling high-dimensional data. However, there remains a need for models that can be reliably fit in modest sample sizes a…
Gridding and Parameter Expansion for Scalable Latent Gaussian Models of Spatial Multivariate Data
Michele Peruzzi, Sudipto Banerjee, David B. Dunson +1
Scalable spatial GPs for massive datasets can be built via sparse Directed Acyclic Graphs (DAGs) where a small number of directed edges is sufficient to flexibly characterize spati…