323 citations · 2.7k across the 27 of their papers we have counts for
28 papers
Posterior exploration for computationally intensive forward models
Mikkel B. Lykkegaard, Colin Fox, Dave Higdon +2
In this chapter, we address the challenge of exploring the posterior distributions of Bayesian inverse problems with computationally intensive forward models. We consider various m…
Non-Stationary Spatial Modeling
Dave Higdon, Jenise Swall, John Kern
Standard geostatistical models assume stationarity and rely on a variogram model to account for the spatial dependence in the observed data. In some instances, this assumption that…
The Mira-Titan Universe IV. High Precision Power Spectrum Emulation
Kelly R. Moran, Katrin Heitmann, Earl Lawrence +6
Modern cosmological surveys are delivering datasets characterized by unprecedented quality and statistical completeness; this trend is expected to continue into the future as new g…
Active Learning for Deep Gaussian Process Surrogates
Annie Sauer, Robert B. Gramacy, David Higdon
Deep Gaussian processes (DGPs) are increasingly popular as predictive models in machine learning (ML) for their non-stationary flexibility and ability to cope with abrupt regime ch…
Analyzing Stochastic Computer Models: A Review with Opportunities
Evan Baker, Pierre Barbillon, Arindam Fadikar +10
In modern science, computer models are often used to understand complex phenomena, and a thriving statistical community has grown around analyzing them. This review aims to bring a…
Discovery of Physics from Data: Universal Laws and Discrepancies
Brian M. de Silva, David M. Higdon, Steven L. Brunton +1
Machine learning (ML) and artificial intelligence (AI) algorithms are now being used to automate the discovery of physics principles and governing equations from measurement data a…