Shiwei Lan

TL;DR: In this article, the authors proposed a framework to integrate global observations and high-resolution simulations in an Earth system model (ESM) that systematically learns from both and quantifies uncertainties.

TL;DR: This work combines geometric methods on a finite-dimensional subspace with mesh-independent infinite-dimensional approaches to speed up MCMC mixing times, while retaining robust mixing times as the dimension grows by using pCN-like methods in the complementary subspace.

TL;DR: It is shown that both of these splitting approaches can reduce the computational cost of sampling from the posterior distribution for a logistic regression model, using either a Gaussian approximation centered on the posterior mode, or a Hamiltonian split into a term that depends on only a small number of critical cases, and another term that involves the larger number of cases whose influence on the anterior distribution is small.

TL;DR: In this paper, the authors proposed a framework to integrate global observations and local high-resolution simulations in an Earth system model (ESM) that systematically learns from both, through matching low-order statistics between ESMs, observations, and high resolution simulations.

TL;DR: This paper proposes the use of statistical experiment design methods to refine a potentially arbitrarily initialized design online without destroying the convergence of the resulting Markov chain to the desired invariant measure.