Showing math.NAShow all
2 papers · 1 filter
math.NA2026
Posterior error bounds for prior-driven balancing in linear Gaussian inverse problems
Josie König, Han Cheng Lie
In large-scale Bayesian inverse problems, it is often necessary to apply approximate forward models to reduce the cost of forward model evaluations, while controlling approximation…
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…