论文标题
通过高维贝叶斯优化的随机最大似然
Randomized Maximum Likelihood via High-Dimensional Bayesian Optimization
论文作者
论文摘要
高维贝叶斯逆问题的后抽样是现实应用程序中的常见挑战。随机最大似然（RML）是一种基于优化的方法，可从近似到后验分布。我们基于高斯工艺（GP）替代模型开发了高维贝叶斯优化方法（BO）方法来解决RML问题。与替代优化方法相比，我们证明了方法的好处，包括多种合成和现实的贝叶斯逆问题，包括医疗和磁流失动力学应用。
Posterior sampling for high-dimensional Bayesian inverse problems is a common challenge in real-world applications. Randomized Maximum Likelihood (RML) is an optimization based methodology that gives samples from an approximation to the posterior distribution. We develop a high-dimensional Bayesian Optimization (BO) approach based on Gaussian Process (GP) surrogate models to solve the RML problem. We demonstrate the benefits of our approach in comparison to alternative optimization methods on a variety of synthetic and real-world Bayesian inverse problems, including medical and magnetohydrodynamics applications.
