mlsanityData to 7 Oct 2026

Loopy Belief Propagation for Approximate Inference: An Empirical Study

arXiv 1301.6725 ↗First posted 23 Jan 2013cs.AI cs.LGcited by 76

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Abstract (arXiv)

Recently, researchers have demonstrated that loopy belief propagation - the use of Pearls polytree algorithm IN a Bayesian network WITH loops OF error- correcting codes.The most dramatic instance OF this IS the near Shannon - limit performance OF Turbo Codes codes whose decoding algorithm IS equivalent TO loopy belief propagation IN a chain - structured Bayesian network. IN this paper we ask : IS there something special about the error - correcting code context, OR does loopy propagation WORK AS an approximate inference schemeIN a more general setting? We compare the marginals computed using loopy propagation TO the exact ones IN four Bayesian network architectures, including two real - world networks : ALARM AND QMR.We find that the loopy beliefs often converge AND WHEN they do, they give a good approximation TO the correct marginals.However,ON the QMR network, the loopy beliefs oscillated AND had no obvious relationship TO the correct posteriors. We present SOME initial investigations INTO the cause OF these oscillations, AND show that SOME simple methods OF preventing them lead TO the wrong results.

Key facts. Cited by 76 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jan 2013.

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Citing papers per year: 2017: 1; 2018: 0; 2019: 0; 2020: 0; 2021: 20; 2022: 20; 2023: 12; 2024: 11; 2025: 3; 2026: 9.

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