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math.NA2018
Multilevel Double Loop Monte Carlo and Stochastic Collocation Methods with Importance Sampling for Bayesian Optimal Experimental Design
Joakim Beck, Ben Mansour Dia, Luis F. R. Espath +1
An optimal experimental set-up maximizes the value of data for statistical inferences and predictions. The efficiency of strategies for finding optimal experimental set-ups is part…
math.NA2018
Nesterov-aided Stochastic Gradient Methods using Laplace Approximation for Bayesian Design Optimization
Andre Gustavo Carlon, Ben Mansour Dia, Luis FR Espath +2
Finding the best setup for experiments is the primary concern for Optimal Experimental Design (OED). Here, we focus on the Bayesian experimental design problem of finding the setup…