44 citations · 250 across the 61 of their papers we have counts for
5 papers · 1 filter
Scalable Physics-based Maximum Likelihood Estimation using Hierarchical Matrices
Yian Chen, Mihai Anitescu
Physics-based covariance models provide a systematic way to construct covariance models that are consistent with the underlying physical laws in Gaussian process analysis. The unkn…
Scalable Computations for Nonstationary Gaussian Processes
Paul G. Beckman, Christopher J. Geoga, Michael L. Stein +1
Nonstationary Gaussian process models can capture complex spatially varying dependence structures in spatial datasets. However, the large number of observations in modern datasets…
Solving Optimal Experimental Design with Sequential Quadratic Programming and Chebyshev Interpolation
Jing Yu, Mihai Anitescu
We propose an optimization algorithm to compute the optimal sensor locations in experimental design in the formulation of Bayesian inverse problems, where the parameter-to-observab…
Scalable Gaussian Process Computations Using Hierarchical Matrices
Christopher J. Geoga, Mihai Anitescu, Michael L. Stein
We present a kernel-independent method that applies hierarchical matrices to the problem of maximum likelihood estimation for Gaussian processes. The proposed approximation provide…
A fast algorithm for maximum likelihood estimation of mixture proportions using sequential quadratic programming
Youngseok Kim, Peter Carbonetto, Matthew Stephens +1
Maximum likelihood estimation of mixture proportions has a long history, and continues to play an important role in modern statistics, including in development of nonparametric emp…