3 papers
math.NA2026
Rational approximation and intrinsic Gaussian processes
Christopher Beattie, David Higdon, Leanna House +2
Gaussian processes (GPs) defined through intrinsic random fields provide a flexible framework for modeling spatial phenomena, and have been advocated in a variety of applications o…
stat.AP2025
Bayesian Deep Gaussian Processes for Correlated Functional Data: A Case Study in Cosmological Matter Power Spectra
Stephen A. Walsh, Annie S. Booth, David Higdon +3
Understanding the structure of our universe and the distribution of matter is an area of active research. As cosmological surveys grow in complexity, the development of emulators t…
stat.AP2024
Towards Improved Uncertainty Quantification of Stochastic Epidemic Models Using Sequential Monte Carlo
Arindam Fadikar, Abby Stevens, Nicholson Collier +6
Sequential Monte Carlo (SMC) algorithms represent a suite of robust computational methodologies utilized for state estimation and parameter inference within dynamical systems, part…