Gauging variational inference*This article is an updated version of: Ahn S, Chertkov M and Shin J 2017 Gauging variational inference Advances in Neural Information Processing Systems 30 ed I Guyon, U V Luxburg, S Bengio, H Wallach, R Fergus, S Vishwanathan and R Garnett (Red Hook, NY: Curran Associates, Inc.) pp 2881–2890. (20th December 2019) - Ahn, Sungsoo; Chertkov, Michael; Shin, Jinwoo
Abstract: Computing of partition function is the most important statistical inference task arising in applications of graphical models (GM). Since it is computationally intractable, approximate methods have been used in practice, where mean-field (MF) and belief propagation (BP) are arguably the most popular and successful approaches of a variational type. In this paper, we propose two new variational schemes, coined Gauged-MF (G-MF) and Gauged-BP (G-BP), improving MF and BP, respectively. Both provide lower bounds for the partition function by utilizing the so-called gauge transformation which modifies factors of GM while keeping the partition function invariant. Moreover, we prove that both G-MF and G-BP are exact for GMs with a single loop of a special structure, even though the bare MF and BP perform badly in this case. Our extensive experiments indeed confirm that the proposed algorithms outperform and generalize MF and BP.