45 citations · 341 across the 31 of their papers we have counts for
6 papers · 1 filter
Multi-group Gaussian Processes
Didong Li, Andrew Jones, Sudipto Banerjee +1
Gaussian processes (GPs) are pervasive in functional data analysis, machine learning, and spatial statistics for modeling complex dependencies. Modern scientific data sets are typi…
Modeling Massive Spatial Datasets Using a Conjugate Bayesian Linear Regression Framework
Sudipto Banerjee
Geographic Information Systems (GIS) and related technologies have generated substantial interest among statisticians with regard to scalable methodologies for analyzing large spat…
Inference for Gaussian Processes with Matérn Covariogram on Compact Riemannian Manifolds
Didong Li, Wenpin Tang, Sudipto Banerjee
Gaussian processes are widely employed as versatile modelling and predictive tools in spatial statistics, functional data analysis, computer modelling and diverse applications of m…
Hierarchical Multivariate Directed Acyclic Graph Auto-Regressive (MDAGAR) models for spatial diseases mapping
Leiwen Gao, Abhirup Datta, Sudipto Banerjee
Disease mapping is an important statistical tool used by epidemiologists to assess geographic variation in disease rates and identify lurking environmental risk factors from spatia…
A Compartment Model of Human Mobility and Early Covid-19 Dynamics in NYC
Ian Frankenburg, Sudipto Banerjee
In this paper, we build a mechanistic system to understand the relation between a reduction in human mobility and Covid-19 spread dynamics within New York City. To this end, we pro…
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…