Modern Bayesian experimental design
Bayesian experimental design (BED) provides a powerful and general framework for
optimizing the design of experiments. However, its deployment often poses substantial …
optimizing the design of experiments. However, its deployment often poses substantial …
Optimal experimental design for infinite-dimensional Bayesian inverse problems governed by PDEs: A review
A Alexanderian - Inverse Problems, 2021 - iopscience.iop.org
We present a review of methods for optimal experimental design (OED) for Bayesian inverse
problems governed by partial differential equations with infinite-dimensional parameters …
problems governed by partial differential equations with infinite-dimensional parameters …
Optimal experimental design: Formulations and computations
Questions of 'how best to acquire data'are essential to modelling and prediction in the
natural and social sciences, engineering applications, and beyond. Optimal experimental …
natural and social sciences, engineering applications, and beyond. Optimal experimental …
Optimizing sequential experimental design with deep reinforcement learning
Bayesian approaches developed to solve the optimal design of sequential experiments are
mathematically elegant but computationally challenging. Recently, techniques using …
mathematically elegant but computationally challenging. Recently, techniques using …
On the convergence of the Laplace approximation and noise-level-robustness of Laplace-based Monte Carlo methods for Bayesian inverse problems
The Bayesian approach to inverse problems provides a rigorous framework for the
incorporation and quantification of uncertainties in measurements, parameters and models …
incorporation and quantification of uncertainties in measurements, parameters and models …
Bayesian sequential optimal experimental design for nonlinear models using policy gradient reinforcement learning
We present a mathematical framework and computational methods for optimally designing a
finite sequence of experiments. This sequential optimal experimental design (sOED) …
finite sequence of experiments. This sequential optimal experimental design (sOED) …
A fast and scalable computational framework for large-scale high-dimensional Bayesian optimal experimental design
We develop a fast and scalable computational framework to solve Bayesian optimal
experimental design problems governed by partial differential equations (PDEs) with …
experimental design problems governed by partial differential equations (PDEs) with …
Active bayesian causal inference
Causal discovery and causal reasoning are classically treated as separate and consecutive
tasks: one first infers the causal graph, and then uses it to estimate causal effects of …
tasks: one first infers the causal graph, and then uses it to estimate causal effects of …
Large-scale Bayesian optimal experimental design with derivative-informed projected neural network
We address the solution of large-scale Bayesian optimal experimental design (OED)
problems governed by partial differential equations (PDEs) with infinite-dimensional …
problems governed by partial differential equations (PDEs) with infinite-dimensional …
Goal-oriented optimal design of experiments for large-scale Bayesian linear inverse problems
We develop a framework for goal-oriented optimal design of experiments (GOODE) for large-
scale Bayesian linear inverse problems governed by PDEs. This framework differs from …
scale Bayesian linear inverse problems governed by PDEs. This framework differs from …