Approximation of differential entropy in Bayesian optimal experimental design
The study tackles large-scale inference problems by proposing a computational method to approximate differential entropy in Bayesian optimal experimental design. By reducing expected information gain to maximum entropy estimation, it uses Monte Carlo or quasi-Monte Carlo surrogates for evidence density. This approach achieves convergence rates comparable to state-of-the-art methods, proving efficient even when likelihood evaluations are expensive.