Bayesian inverse problems constitute an important methodological part of parameter studies in predictive modeling. Since high-dimensional parameter spaces often require a huge computational effort, approaches for cost reduction are of fundamental importance. For example, the costs can be substantially reduced by exploiting low-dimensional structure in the form of active subspaces. In this work, we derive new generalized bounds, develop an iterative algorithm, and demonstrate computational benefits with models from various applied disciplines.
«
Bayesian inverse problems constitute an important methodological part of parameter studies in predictive modeling. Since high-dimensional parameter spaces often require a huge computational effort, approaches for cost reduction are of fundamental importance. For example, the costs can be substantially reduced by exploiting low-dimensional structure in the form of active subspaces. In this work, we derive new generalized bounds, develop an iterative algorithm, and demonstrate computational benefi...
»