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Found 5 papers, 3 papers with code
Dynamic Bayesian Learning and Calibration of Spatiotemporal Mechanistic Systems
Through reduced-rank Gaussian processes and a conjugate model specification, our methodology is applicable to large-scale calibration and inverse problems.
Modeling Massive Spatial Datasets Using a Conjugate Bayesian Linear Regression Framework
Geographic Information Systems (GIS) and related technologies have generated substantial interest among statisticians with regard to scalable methodologies for analyzing large spatial datasets.
Bayesian Hierarchical Modeling and Analysis for Actigraph Data from Wearable Devices
The majority of Americans fail to achieve recommended levels of physical activity, which leads to numerous preventable health problems such as diabetes, hypertension, and heart diseases.
Gaussian Processes
Applications
Methodology
Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domains
Unlike some existing models for large spatial data, a Q-MGP facilitates massive caching of expensive matrix operations, making it particularly apt in dealing with spatiotemporal remote-sensing data.
Methodology Computation
On identifiability and consistency of the nugget in Gaussian spatial process models
We formally establish results on the identifiability and consistency of the nugget in spatial models based upon the Gaussian process within the framework of in-fill asymptotics, i. e. the sample size increases within a sampling domain that is bounded.
Spatial Interpolation
Statistics Theory
Statistics Theory
