A1499
Title: A review in Bayesian structure learning in Gaussian graphical models
Authors: Lucas Vogels - University of Amsterdam (Netherlands) [presenting]
Reza Mohammadi - University of Amsterdam (Netherlands)
Marit Schoonhoven - University of Amsterdam (Netherlands)
Ilker Birbil - University of Amsterdam (Netherlands)
Abstract: Gaussian graphical models provide a powerful framework to reveal the conditional dependency structure between multivariate variables. The process of uncovering the conditional dependency network is known as structure learning. Bayesian methods can measure the uncertainty of conditional relationships and include prior information. However, frequentist methods are often preferred due to the computational burden of the Bayesian approach. Over the last decade, Bayesian methods have seen substantial improvements, with some now capable of generating accurate estimates of graphs up to a thousand variables in mere minutes. Despite these advancements, a comprehensive review or empirical comparison of all recent methods has not been conducted. The aim is to delve into a wide spectrum of Bayesian approaches used for structure learning and evaluate their efficacy through a comprehensive simulation study. The application of Bayesian structure learning is also demonstrated in a real-world data set, and directions for future research are provided. An exhaustive overview of this dynamic field is given for newcomers, practitioners, and experts.
