期刊
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
卷 45, 期 4, 页码 5139-5157出版社
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2022.3196140
关键词
Computational modeling; Belief propagation; Probabilistic logic; Convergence; Graphical models; Couplings; Random variables; belief propagation; probabilistic inference; sum-product algorithm; partition function; inference algorithms
Self-guided belief propagation (SBP) is an enhanced version of belief propagation that gradually incorporates pairwise potentials, increasing accuracy without increasing computational burden. SBP finds the global optimum of the Bethe approximation for attractive models and obtains a unique, stable, and accurate solution when BP does not converge.
Belief propagation (BP) is a popular method for performing probabilistic inference on graphical models. In this work, we enhance BP and propose self-guided belief propagation (SBP) that incorporates the pairwise potentials only gradually. This homotopy continuation method converges to a unique solution and increases the accuracy without increasing the computational burden. We provide a formal analysis to demonstrate that SBP finds the global optimum of the Bethe approximation for attractive models where all variables favor the same state. Moreover, we apply SBP to various graphs with random potentials and empirically show that: (i) SBP is superior in terms of accuracy whenever BP converges, and (ii) SBP obtains a unique, stable, and accurate solution whenever BP does not converge.
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