4 papers
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…
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…
Bayesian "Deep" Process Convolutions: An Application in Cosmology
Kelly R. Moran, Richard Payne, Earl Lawrence +7
The nonlinear matter power spectrum in cosmology describes how matter density fluctuations vary with scale in the universe, providing critical insights into large-scale structure f…
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…