An approximate empirical Bayesian method for large-scale linear-Gaussian inverse problems

Qingping Zhou, Wenqing Liu, Jinglai Li, Youssef M Marzouk

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


We study Bayesian inference methods for solving linear inverse problems, focusing on hierarchical formulations where the prior or the likelihood function depend on unspecified hyperparameters. In practice, these hyperparameters are often determined via an empirical Bayesian method that maximizes the marginal likelihood function, i.e. the probability density of the data conditional on the hyperparameters. Evaluating the marginal likelihood, however, is computationally challenging for large-scale problems. In this work, we present a method to approximately evaluate marginal likelihood functions, based on a low-rank approximation of the update from the prior covariance to the posterior covariance. We show that this approximation is optimal in a minimax sense. Moreover, we provide an efficient algorithm to implement the proposed method, based on a combination of the randomized SVD and a spectral approximation method to compute square roots of the prior covariance matrix. Several numerical examples demonstrate good performance of the proposed method.
Original languageEnglish
Article number095001
JournalInverse Problems
Issue number9
Early online date4 May 2018
Publication statusPublished - Sept 2018


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