2 papers
math.NA2026
Error bounds for approximate posteriors from likelihood-informed reduced-order models
Han Cheng Lie, Jakob Scheffels, Elisabeth Ullmann
In the design of computational methods for Bayesian inverse problems, costly forward model evaluations make it difficult to sample from or compute the posterior. This motivates the…
math.NA2026
Likelihood-informed Model Reduction for Bayesian Inference of Static Structural Loads
Jakob Scheffels, Elizabeth Qian, Iason Papaioannou +1
Bayesian inverse problems use data to update a prior probability distribution on uncertain parameter values to a posterior distribution. Such problems arise in many structural engi…