Belief propagation (BP) and the Bethe approximation are two closely relatedconcepts that both suffer from the existence of multiple fixed points (or stationarypoints). We propose a modification of BP, termed self-guided belief propagation(SBP), that incorporates the pairwise potentials only gradually; this essentiallyselects one specific fixed point and increases the accuracy without increasing thecomputational burden. We apply SBP to various models with Ising potentials andshow 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.
|Number of pages||6|
|Publication status||Published - 14 Dec 2019|
|Event||Machine Learning and the Physical Sciences - Vancouver, Canada|
Duration: 14 Dec 2019 → 14 Dec 2019
|Conference||Machine Learning and the Physical Sciences|
|Period||14/12/19 → 14/12/19|