Pages that link to "Item:Q2309392"
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The following pages link to Nesterov-aided stochastic gradient methods using Laplace approximation for Bayesian design optimization (Q2309392):
Displaying 12 items.
- A Laplace method for under-determined Bayesian optimal experimental designs (Q1798603) (← links)
- Constructing unbiased gradient estimators with finite variance for conditional stochastic optimization (Q2095692) (← links)
- Multimodal information gain in Bayesian design of experiments (Q2135895) (← links)
- Variational Bayesian approximation of inverse problems using sparse precision matrices (Q2138759) (← links)
- An engineering interpretation of Nesterov's convex minimization algorithm and time integration: application to optimal fiber orientation (Q2666090) (← links)
- Small-noise approximation for Bayesian optimal experimental design with nuisance uncertainty (Q2674073) (← links)
- Unbiased MLMC Stochastic Gradient-Based Optimization of Bayesian Experimental Designs (Q5028414) (← links)
- Nested-Batch-Mode Learning and Stochastic Optimization with An Application to Sequential MultiStage Testing in Materials Science (Q5254790) (← links)
- Large-scale Bayesian optimal experimental design with derivative-informed projected neural network (Q6159007) (← links)
- Modern Bayesian experimental design (Q6540235) (← links)
- Multilevel double loop Monte Carlo and stochastic collocation methods with importance sampling for Bayesian optimal experimental design (Q6553495) (← links)
- Optimal experimental design: formulations and computations (Q6598420) (← links)
