62 citations · 268 across the 38 of their papers we have counts for
7 papers · 1 filter
Bayesian Non-Parametric Factor Analysis for Longitudinal Spatial Surfaces
Samuel I. Berchuck, Mark Janko, Felipe A. Medeiros +2
We introduce a Bayesian non-parametric spatial factor analysis model with spatial dependency induced through a prior on factor loadings. For each column of the loadings matrix, spa…
A Case for Quantifying Statistical Robustness of Specialized Probabilistic AI Accelerators
Xiangyu Zhang, Sayan Mukherjee, Alvin R. Lebeck
Statistical machine learning often uses probabilistic algorithms, such as Markov Chain Monte Carlo (MCMC), to solve a wide range of problems. Many accelerators are proposed using s…
Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma
Samuel I. Berchuck, Felipe A. Medeiros, Sayan Mukherjee
As big spatial data becomes increasingly prevalent, classical spatiotemporal (ST) methods often do not scale well. While methods have been developed to account for high-dimensional…
Adaptive particle-based approximations of the Gibbs posterior for inverse problems
Zilong Zou, Sayan Mukherjee, Harbir Antil +1
In this work, we adopt a general framework based on the Gibbs posterior to update belief distributions for inverse problems governed by partial differential equations (PDEs). The G…
Maximum -free subgraphs
Dhruv Mubayi, Sayan Mukherjee
Given a family of hypergraphs , let denote the largest size of an -free subgraph that one is guaranteed to find in every hypergraph with $…
Bayesian Multinomial Logistic Normal Models through Marginally Latent Matrix-T Processes
Justin D. Silverman, Kimberly Roche, Zachary C. Holmes +2
Bayesian multinomial logistic-normal (MLN) models are popular for the analysis of sequence count data (e.g., microbiome or gene expression data) due to their ability to model multi…