A stochastic cost-benefit greedy algorithm with approximation guarantees is proposed for multi-type sensor placement in linear Bayesian inverse problems, and a non-intrusive BAE-based lower bound on EIG is used for nonlinear problems.
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Multi-type Sensor Placement for PDE-based Bayesian Inverse Problems
A stochastic cost-benefit greedy algorithm with approximation guarantees is proposed for multi-type sensor placement in linear Bayesian inverse problems, and a non-intrusive BAE-based lower bound on EIG is used for nonlinear problems.